Modeling and Diagnosis of Friction and Wear in Industrial Robots

Modeling and Diagnosis of Friction and Wear in Industrial Robots
Linköping studies in science and technology. Dissertations.
No. 1617
Modeling and Diagnosis of Friction
and Wear in Industrial Robots
André Carvalho Bittencourt
Department of Electrical Engineering
Linköping University, SE–581 83 Linköping, Sweden
Linköping 2014
Cover illustration: Friction curves for different values of temperature, load and
wear. The RGB color used in each curve corresponds to the value of temperature
(red, [30 − 80]◦ C), load (green, [0 − 100]%) and wear (blue, [0 − 50]%).
Linköping studies in science and technology. Dissertations.
No. 1617
Modeling and Diagnosis of Friction and Wear in Industrial Robots
André Carvalho Bittencourt
andrecb@isy.liu.se
www.control.isy.liu.se
Division of Automatic Control
Department of Electrical Engineering
Linköping University
SE–581 83 Linköping
Sweden
ISBN 978-91-7519-251-2
ISSN 0345-7524
Copyright © 2014 André Carvalho Bittencourt
Printed by LiU-Tryck, Linköping, Sweden 2014
To the memory of my brother.
Abstract
High availability and low operational costs are critical for industrial systems.
While industrial equipments are designed to endure several years of uninterrupted operation, their behavior and performance will eventually deteriorate
over time. To support service and operation decisions, it is important to devise
methods to infer the condition of equipments from available data.
The monitoring of industrial robots is an important problem considered in this
thesis. The main focus is on the design of methods for the detection of excessive
degradations due to wear in a robot joint. Since wear is related to friction, an
important idea for the proposed solutions is to analyze the behavior of friction
in the joint to infer about wear. Based on a proposed friction model and friction
data collected from dedicated experiments, a method is suggested to estimate
wear-related effects to friction. As it is shown, the achieved estimates allow for
a clear distinction of the wear effects even in the presence of large variations to
friction associated to other variables, such as temperature and load.
In automated manufacturing, a continuous and repeatable operation of equipments is important to achieve production requirements. Such repetitive behavior
of equipments is explored to define a data-driven approach to diagnosis. Considering data collected from a repetitive operation, an abnormality is inferred by
comparing nominal against monitored data in the distribution domain. The approach is demonstrated with successful applications for the diagnosis of wear in
industrial robots and gear faults in a rotating machine.
Because only limited knowledge can be embedded in a fault detection method,
it is important to evaluate solutions in scenarios of practical relevance. A simulation based framework is proposed that allows for determination of which variables affect a fault detection method the most and how these variables delimit the
effectiveness of the solution. Based on an average performance criterion, an approach is also suggested for a direct comparison of different methods. The ideas
are illustrated for the robotics application, revealing properties of the problem
and of different fault detection solutions.
An important task in fault diagnosis is a correct determination of presence of a
condition change. An early and reliable detection of an abnormality is important
to support service, giving enough time to perform maintenance and avoid downtime. Data-driven methods are proposed for anomaly detection that only require
availability of nominal data and minimal/meaningful specification parameters
from the user. Estimates of the detection uncertainties are also possible, supporting higher level service decisions. The approach is illustrated with simulations
and real data examples including the robotics application.
v
Populärvetenskaplig sammanfattning
För industriella system är både hög tillgänglighet och låga driftskostnader avgörande. Industriella system är oftast utformad för att klara flera års oavbruten
drift, men över tid kommer beteendet och prestandan så småningom att förändras. Det är därför viktigt att ta fram metoder som kan extrahera information från
tillgänglig data och dra slutsatser om systemets beteende, som i sin tur används
som stöd för beslut angående systemets fortsatta drift.
Denna avhandling handlar om utformning och utvärdering av diagnostiska metoder för att stödja tids- och kostnadseffektiva beslut angående den fortsatta driften
för systemet. I synnerhet studeras problemet med att upptäcka för höga nivåer
av slitage i respektive led för en industrirobot. Eftersom slitage påverkar friktionen kan det vara en bra idé att analysera friktionen för att uppskatta hur stort
slitage som har uppkommit. Baserat på en föreslagen friktionsmodell och friktionsdata från specialanpassade experiment föreslås en metod för att uppskatta
slitagets omfattning. Metoden försöker anpassa modellen så att sannolikheten att
mätningarna kommer från den föreslagna modellen maximeras. Det visar sig att
tillförlitliga beräkningar av slitaget kan uppnås även vid stora variationer i belastningen på roboten samt temperaturen i robotens leder, vilket gör det möjligt
att planera underhåll för roboten innan den går sönder.
Vidare undersöks hur ett systems repetitiva beteende, som är vanligt inom automatiserad tillverkning, kan utnyttjas för att skapa en metod för diagnos som
endast använder befintlig data utan hjälp av någon modell. Med hjälp av data som har samlats in från en repetitiv process kan en förändring av processen
upptäckas genom att jämföra data från systemet i felfri drift och befintlig drift.
Metoden som föreslås utnyttjar den empiriska sannolikhetsfördelningen för systemet i felfri respektive befintlig drift. Det visar sig att metoden med framgång
kan detektera slitage i lederna för en industrirobot samt växelfel i en roterande
mekanism.
I avhandlingen föreslås också metoder för feldetektering. Testet går ut på att man
jämför två hypoteser mot varandra genom ett statistiskt ramverk. För att upptäcka en förändring av ett system är det naturligt att de två hypoteserna motsvarar
ett system utan fel respektive ett system med fel. Det enda som förutsätts är att
data från systemet utan fel är tillgängligt. En annan viktig del är att kunna jämföra olika diagnosmetoder för att se vilken som passar bäst till det aktuella problemet. Ett ramverk baserat på simuleringar har därför föreslagits för utvärdering
av diagnosmetoder. Ramverket kan användas för att avgöra vilka variabler som
påverkar metoden mest, hur man jämför olika metoder samt hur man bestämmer
det effektiva användningsområdet för respektive metod. De föreslagna diagnosmetoderna och ramverket för utvärdering av diagnosmetoderna är generella men
illustreras i avhandlingen på tillämpningar för industrirobotar.
vii
Acknowledgments
I would like to thank my supervisor Svante Gunnarsson for the guidance through
these years, always gentle and prompt in my inquiries. Special thanks also to
my co-supervisors Mikael Norrlöf and Erik Wernholt for the invaluable input.
Thank you Lennart Ljung and Svante Gunnarsson for accepting me in the group
and Shiva Sander Tavallaey for inviting me to graduate education. Being a graduate student at the isy/rt group has been a remarkable experience and I would
like to express my gratitude to everyone behind our organizational structure. To
mention some, thank you Lennart Ljung and Svante Gunnarsson for your leadership; thank you Torkel Glad and Johan Löfberg for your roles in our educational
programs; thank you Martin Enqvist for your availability and kindness; thank
you Ulla Salaneck and Ninna Stensgård for the administrative support; thanks
to all of our gurus, specially the ones behind our LATEX thesis template, Gustav
Hendeby and Henrik Tidefelt. Special thanks for the people that helped me reviewing this thesis, Svante Gunnarsson, Mikael Norrlöf, Patrik Axelsson, Daniel
Eriksson and Emre Özkan.
The close collaboration with abb was very important for the achievements in this
thesis. abb not only supported me financially, via vinnova’s Industry Excellence
Center link-sic, but also with expertise, guidance and friendship. Shiva Sander
Tavallaey played a central role in all stages of the work, before, during and after;
your dear guidance and kindness have been highly esteemed. Special thanks to
Mikael Norrlöf, Kari Saarinen, Hans Andersson, Torgny Brogård and Shiva for all
the fruitful discussions and the invaluable input. Thank you Niclas Sjöstrand for
inviting me to abb in 2007, event which is likely to have sparked much of this,
and for inviting me again in 2012. Thank you Alf Isaksson and Krister Forsman
from Perstorp for our collaborations outside the robotics landscape. Thank you
all for helping me feel home at abb.
The arduous and long journey towards a PhD was eased by the presence of good
friends in my live. I was lucky enough to start together with rt’s indisputable
host, Sina Khoshfetrat Pakazad; your friendship has been an invaluable gift during this period, thanks for everything! Thank you Daniel Ankelhed, Jonas Linder
and Patrik Axelsson for your patience and company as my office mates. Thank
you Tohid Ardeshiri for always keeping an eye on me, but also for your generosity and unlimited excitement for any blow of wind. Speaking of bananas, thanks
Karl Granström for showing me how to ride a mini motorcycle and for valuing
my word more than I do sometimes, I will always regard you highly. Speaking
of motorcycles, thank you Johan Löfberg, a.k.a. JLö, for the chance to enjoy riding again on your spare ktm, hopefully I will be riding my own soon. Probably
sooner than the time it will take me to forget some of the memories from our
planning meetings; thanks for that Fredrik Lindsten and for being a great partner in the misbehaving during the after hours. Unlike Emre Özkan, who spots
bad ideas and moments right away with his telepathic skills; thank you for your
brotherly friendship and for saving my life in Sheffield. Had you not been there
at the right moment, I would not have had the chance to run long distances with
ix
x
Acknowledgments
Martin Skoglund, or to ski, hike, climb and drive 1500km across Scandinavia
with Hanna Nyqvist and Per, who are great people but have a strange taste for
food. Unlike Marek Syldatk, who seems to have a taste for everything and has
never tasted something that was not delicious; thanks for your loyal friendship
and for all the beers we shared as flatmates. Speaking of beers, thanks Jonas Linder for the clockwork timing for Fredagspuben, after skis and after works. The
after parties are naturally acknowledged to Clas Veibäck and Isak Nielsen, just as
it should be acknowledged that Niklas Wahlström is the king’s clarinet but holds
the crown at the dance-floor. Thank you George Mathai for all the philosophical
ventures, Michael Roth for keeping it simple and Patrik Axelsson for keeping it
dependable; thanks for all the help with the PhD checklist PAx. Thank you Peter Rosander, Saikat Saha, Tianshi Chen, Henrik Ohlsson, Umut Orguner, Manon
Kok, Carsten Fritsche, Daniel Petersson, Ylva Jung, Lubos Váci, Zoran Sjanic, Gustav Hendeby, Johan Dahlin and everyone else for all the moments we shared.
Obrigado pai, mãe e irmão pelo suporte e amor incondicionais. Admiro e amo
cada de um vocês e espero poder estar mais presente daqui pra frente. Essa conquista é um simples reflexo da presença de vocês em minha vida. Um grande
abraço e beijo.
Thank you Alicia for all the hugs in the morning, without warning. As you turn
these pages, a new chapter stages. It is you and me again, no reason to abstain.
Yours,
André Carvalho Bittencourt,
Linköping, August 2014.
Contents
Notation
xv
1 Introduction
1.1 Motivation . . . . . . . . . . . . . . . .
1.2 Research Goals and Approach . . . . .
1.3 Thesis Outline . . . . . . . . . . . . . .
1.3.1 Background on the publications
1.3.2 Relevant and additional work .
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2 Basics of Industrial Robotics
2.1 Actuators and Sensors . . . . . . . . . .
2.1.1 Basic setup . . . . . . . . . . . .
2.1.2 Application dependent sensors
2.2 Modeling . . . . . . . . . . . . . . . . .
2.2.1 Kinematics . . . . . . . . . . . .
2.2.2 Dynamics . . . . . . . . . . . .
2.3 Identification . . . . . . . . . . . . . . .
2.4 Reference Generation and Control . . .
2.5 Summary and Connections . . . . . . .
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3 Joint Friction and Wear
3.1 Basics of Tribology . . . . . . . . . . . .
3.2 Friction Dependencies in Robot joints .
3.3 Modeling . . . . . . . . . . . . . . . . .
3.4 Summary and Connections . . . . . . .
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4 Basics of Fault Diagnosis
4.1 Overview of Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Fault detection . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.2 Models of systems and faults . . . . . . . . . . . . . . . . .
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I Background
xi
Contents
xii
4.2 Fault Detection Algorithms . . . . . . . . . . . . . . . . . . .
4.2.1 Parameter estimation . . . . . . . . . . . . . . . . . .
4.2.2 Signal-driven methods . . . . . . . . . . . . . . . . .
4.2.3 Data-driven methods . . . . . . . . . . . . . . . . . .
4.3 Decision Rule . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Thresholding . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Likelihood ratio tests . . . . . . . . . . . . . . . . . .
4.3.3 Statistical significance tests . . . . . . . . . . . . . .
4.3.4 Compromises between errors and time of detection
4.4 Summary and Connections . . . . . . . . . . . . . . . . . . .
5 Conclusions and Discussion
5.1 Conclusions of Part I . . . . . . . . . .
5.2 Summary and Discussion for Part II .
5.3 Conclusions . . . . . . . . . . . . . . .
5.4 Recommendations for Future Research
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Bibliography
65
II Publications
A Friction in a Robot Joint
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Identification of Friction Models . . . . . . . . . . . . . . . . .
2.1
Covariance estimate . . . . . . . . . . . . . . . . . . . .
3
Basics of Friction Phenomena in a Robot Joint . . . . . . . . .
3.1
A procedure to estimate friction at a fixed speed level
3.2
Modeling of velocity dependencies . . . . . . . . . . .
4
Empirically Motivated Modeling . . . . . . . . . . . . . . . .
4.1
Guidelines for the experiments . . . . . . . . . . . . .
4.2
Effects of joint angles . . . . . . . . . . . . . . . . . . .
4.3
Effects of load torques . . . . . . . . . . . . . . . . . .
4.4
Effects of temperature . . . . . . . . . . . . . . . . . .
4.5
A complete model . . . . . . . . . . . . . . . . . . . . .
4.6
Validation . . . . . . . . . . . . . . . . . . . . . . . . .
5
Conclusions and Further Research . . . . . . . . . . . . . . . .
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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B Modeling and Identification of Wear in a Robot Joint
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Steady-State Friction in a Robot Joint . . . . . . . . . . . . . .
2.1
A procedure to estimate friction at a fixed speed level
2.2
A model for the nominal behavior of friction . . . . .
2.3
A model for the effects of wear to friction . . . . . . .
2.4
A complete model of steady-state friction . . . . . . .
3
Model-Based Wear Estimation . . . . . . . . . . . . . . . . . .
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Contents
xiii
3.1
Maximum likelihood estimation . . . . . . . . . . . .
3.2
Experiment design . . . . . . . . . . . . . . . . . . .
4
Simulation Study . . . . . . . . . . . . . . . . . . . . . . . .
4.1
Definition of parameters used . . . . . . . . . . . . .
4.2
Experiment design . . . . . . . . . . . . . . . . . . .
4.3
Bias and variance properties of the wear estimators .
5
Studies based on Real Data . . . . . . . . . . . . . . . . . . .
5.1
Description of scenarios . . . . . . . . . . . . . . . .
5.2
Results and discussion . . . . . . . . . . . . . . . . .
6
Conclusions and Future Work . . . . . . . . . . . . . . . . .
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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C Data-Driven Diagnostics of Repetitive Processes
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Data-Driven Diagnostics and Repetitive Systems . . . . . .
2.1
Detection, performance and isolation . . . . . . . . .
2.2
Repetitive systems . . . . . . . . . . . . . . . . . . .
3
A Distribution Domain Approach . . . . . . . . . . . . . . .
3.1
Characterizing the data – Kernel Density Estimate .
3.2
Comparing sequences – Kullback-Leibler distance .
3.3
Handling non-repetitive disturbances and noise . .
4
Wear Monitoring in an Industrial Robot Joint . . . . . . . .
4.1
Experimental studies under constant disturbances .
4.2
Simulation studies under temperature disturbances
5
Gearbox Monitoring based on Vibration Data . . . . . . . .
6
Conclusions and Future Work . . . . . . . . . . . . . . . . .
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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D Simulation based Evaluation of Fault Detection Algorithms
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
1.1
Problem description and motivation . . . . . . .
1.2
Main contributions and outline . . . . . . . . . .
2
Design of Experiments . . . . . . . . . . . . . . . . . . .
2.1
Choice of input factors . . . . . . . . . . . . . . .
2.2
Surrogate models as linear regressions . . . . . .
2.3
Identification . . . . . . . . . . . . . . . . . . . .
2.4
Design matrix . . . . . . . . . . . . . . . . . . . .
2.5
Design parameters . . . . . . . . . . . . . . . . .
2.6
Model validation . . . . . . . . . . . . . . . . . .
3
Determining Relevant Factors . . . . . . . . . . . . . . .
3.1
Normalization of coefficients . . . . . . . . . . .
3.2
Group analysis . . . . . . . . . . . . . . . . . . . .
4
Comparing Fault Detection Algorithms . . . . . . . . . .
4.1
Two hypotheses . . . . . . . . . . . . . . . . . . .
4.2
A measure of average effects . . . . . . . . . . . .
4.3
Group analysis . . . . . . . . . . . . . . . . . . . .
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Contents
xiv
5
Determining the Effective Scope . . . . . . . . . . .
5.1
A measure of satisfactory performance . . .
5.2
Finding the effective scope . . . . . . . . . .
5.3
Group analysis . . . . . . . . . . . . . . . . .
6
Evaluation of fdas for Wear Monitoring in Robots
6.1
Design of experiments . . . . . . . . . . . .
6.2
Determining relevant factors . . . . . . . .
6.3
Comparing fault detection algorithms . . .
6.4
Determining the effective scope . . . . . . .
7
Conclusions . . . . . . . . . . . . . . . . . . . . . .
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . .
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170
170
171
171
172
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180
181
E Data-Driven Anomaly Detection based on a Bias Change
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
The Bias Change Model and the glr test . . . . . . . . . . . .
2.1
Unknown change time . . . . . . . . . . . . . . . . . .
2.2
Sequential solution . . . . . . . . . . . . . . . . . . . .
2.3
Asymptotic performance . . . . . . . . . . . . . . . . .
3
Nonparametric Density Estimators . . . . . . . . . . . . . . .
3.1
Kernel density estimator . . . . . . . . . . . . . . . . .
3.2
A sparse density estimator . . . . . . . . . . . . . . . .
4
Estimating the Bias Change . . . . . . . . . . . . . . . . . . .
4.1
Batch estimation using em . . . . . . . . . . . . . . . .
4.2
Sequential estimation using stochastic approximation
5
Illustrative Examples . . . . . . . . . . . . . . . . . . . . . . .
5.1
Simulation study . . . . . . . . . . . . . . . . . . . . .
5.2
Batch detection of an increase in eruptions . . . . . .
5.3
Sequential detection of wear in a robot joint . . . . . .
6
Conclusions and Future Work . . . . . . . . . . . . . . . . . .
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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183
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203
206
Notation
Abbreviations
Abbreviation
iso
abb
sram
cbm
kld
crb
kde
roc
dof
bl, ml, ehl
Meaning
International Organization for Standardization.
Asea Brown Boveri Ltd.
Safety, Reliability, Availability and Maintainability.
Condition Based Maintenance.
Kullback-Leibler Divergence.
Cramér-Rao lower Bound.
Kernel Density Estimate.
Receiver Operating Characteristic.
Degree of Freedom.
Boundary, Mixed and Elasto-Hydrodynamic Lubrication regions of the friction curve.
Basic Mathematical Notation
Notation
x∈X
x ∈ Xn
xi
X ∈ Xn×m
xij
[X]ij
Xi:j
Xj
f (x) : X 7→ Y
f (x) : X 7→ Yn
Meaning
Scalar quantity from set X.
Column vector of size n with elements in X.
The ith element of vector x.
Matrix with n rows and m columns with elements in X.
The element of X in the ith row and jth column.
Alternative notation for xij .
Submatrix of X composed of columns from i to j.
Shorthand notation for X1:j .
Scalar function map.
Vector function map.
xv
Notation
xvi
Operators and Special Functions
Notation
,
∼
∝
x⊙y
|x|
|X|
|X|
k · kδ
XT
hx, yiP
sign(x)
arg min f (x)
x
d
dx f
∂
f
∂x
(x)
(x)
ẋ(t)
T{f (x)}
F{f (x)}
F−1 {f (ν)}
Meaning
Equal by definition.
Denotes “is distributed according to”.
Denotes “is proportional to”.
Denotes the Hadamard, element-wise, multiplication.
Modulus of x.
The determinant of matrix X.
The cardinality (number of elements) of set X.
The δ vector or induced matrix norm.
The transpose of matrix X, i.e., Y = X T implies yij = xj i .
Denotes the weighted inner product x T Py.
The function satisfying x = sign(x)|x| and sign(0) = 0.
The value of x that minimizes f (x).
Derivative of f (x) with respect to x.
Gradient of f (x) with respect to x.
Derivative of x(t) with respect to time.
Integral transform of f (x).
Fourier transform of f (x).
Inverse Fourier transform of f (ν).
Notation for Probability, Statistics and Decision Theory
Notation
Meaning
y
Pr [Y ∈ A]
p(y)
Sample from the random variable Y .
Probability of an event A.
d
Probability dist. (density) function, dy
Pr[Y ≤ y].
R∞
Expectation of f (y), −∞ f (y)p(y) dy.
Characteristic function, E[e νy ].
The multivariate Gaussian density.
The uniform density with limits y and y.
Kullback-Leibler divergence between p(y) and q(y).
Symmetric Kullback-Leibler divergence, DKL (p||q) +
DKL (q||p).
E[f (y)]
Φ(ν)
N (µ, Σ)
U (y, y)
DKL (p||q)
KL (p, q)
H0
H1
φ(q)
R0
Pf
Pm
Null hypothesis in a binary test.
Alternative hypothesis in a binary test.
Decision function in a binary test, φ(q) : R 7→ {0, 1}.
Acceptance region in a binary test, R0 , {q : φ(q) = 0}.
Probability of incorrectly choosing H1 in a binary test.
Probability of incorrectly choosing H0 in a binary test.
Notation
xvii
Notation for Robotics
Notation
·a
·m
·r
Λ
ϕ
i
τ
τf
τg
τℓ
τp
ξ
̟
℧
J(·)
L( · , · )
K( · , · )
P(·)
M( · )
C( · )
K( · )
D( · )
pi
pi
Ri−1
i
dii−1
Hii−1
x
Meaning
Denotes a quantity described in the arm side.
Denotes a quantity described in the motor side.
Denotes a reference signal.
Inverse gear ratio matrix.
Vector of joint angular positions.
Vector of applied motor currents.
Vector of applied torques.
Vector of joint friction torques.
Vector of gravity-induced torques.
Component of τ g parallel to the joint dof.
Resulting component of τ g perpendicular to τ ℓ .
Joint lubricant temperature.
Joint wear level.
A trajectory.
Analytical Jacobian.
Lagrangian function.
Kinetic energy.
Potential energy.
Inertia matrix.
Matrix of Coriolis and centrifugal torques.
Stiffness matrix.
Damping matrix.
ith coordinate frame.
ith homogeneous coordinate frame.
Rotation from frame i to i −1.
Translation from frame i to i −1.
Homogeneous transformation from frame i to i −1.
End-effector pose (position and orientation).
Notation
xviii
Notation for Friction Modeling
Notation
f
x
g( · )
h( · )
z
σ0 , σ1
fc
fs
fv
fµ
ϕ̇s
α
β
Meaning
Generalized friction.
Generalized friction states vector.
Velocity weakening of the friction curve.
Velocity strengthening of the friction curve.
Internal friction state in a dynamic friction model.
Stiffness and damping parameters of the LuGre model.
Coulomb friction parameter.
Standstill (static) friction parameter.
Viscous friction parameter.
Non-Newtonian viscous friction parameter.
Stribeck speed friction parameter.
Stribeck speed exponent parameter.
Non-Newtonian viscous friction exponent parameter.
Notation
xix
Notation for Models of Systems and Identification
Notation
u
y
r
d
f
z
v
k
N
θ
θ0
b
θ
b
θN
M
M(θ)
φ( · )
Φ( · )
η
ρ
yk
b
y(k|θ)
ǫ(k, θ)
ψ(k, θ)
L(θ)
F (θ′ )
Meaning
Control input vector.
Measured output vector.
Reference vector.
Unknown disturbance vector.
Unknown fault vector.
Deterministic input vector.
Random input vector.
Sample index in N.
Total number of data samples.
Vector of parameters.
True vector of parameters.
Estimate of the parameters θ0 .
Parameter estimate achieved from N samples.
Model structure.
A model instance of M determined by θ.
Regression vector function.
Matrix of stacked regressors.
Parameters that appear linearly in the regression.
Parameters that appear nonlinearly in the regression.
Vector of measurements at index k.
Predictor function at index k.
Prediction error function, y k − b
y(k|θ).
∂
Gradient − ǫ(k, θ).
∂θ
Likelihood function.
Fisher information matrix evaluated at θ′ .
1
Introduction
Driven by the severe competition in a global market, stricter legislation and increase of consumer concerns towards environment and health/safety, industrial
systems are faced with high requirements on safety, reliability, availability, and
maintainability (sram). In the industry, equipment failure is a major factor of accidents and down time (Khan and Abbasi, 1999; Rao, 1998). While a correct specification and design of the equipments are crucial for increased sram (Thompson
(1999)), no amount of design effort can prevent deterioration over time and equipments will eventually fail. However, the associated impacts can be considerably
reduced by appropriate maintenance practices. Fault diagnosis methods can be
used to determine the condition of the equipment, detect and identify faults and
are thus desirable to support service. Fault diagnosis can be used to increase
sram and reduce the overall costs of service, e.g., by allowing for condition-based
maintenance (cbm).
This thesis addresses the design of fault diagnosis methods for an equipment
which is many times of crucial importance in manufacturing, industrial robots.
The main focus is on the monitoring and detection of excessive degradations
caused by wear of the mechanical parts. The wear processes may take several
years to be of significance, but can evolve rapidly once it starts to appear. An
early detection of excessive wear levels can allow for cbm and increased sram.
Since wear is related to friction, the basic idea pursued is to analyze the behavior of friction in order to infer about wear. To allow this, an extensive study of
friction in robot joints is performed and different solutions for detection of wear
related changes are proposed and evaluated. This chapter presents an introduction and motivation to the problem, followed by the outline and main research
contributions of the thesis.
1
1 Introduction
2
(a) Pick and place.
(b) Spot welding.
Figure 1.1: Examples of applications of industrial robots where high availability is critical. The economical damages of an unpredicted robot stop in a
production line are counted by the second.
1.1 Motivation
Industrial robots are used as a key factor to improve productivity, quality and
safety in automated manufacturing. Robot installations are many times of crucial
importance in the processes where they are used. As illustrated by the applications found in Figure 1.1, an unexpected robot stop or malfunction has the potential to cause downtimes of entire production lines, with consequent production
losses and economical damages. Availability and maintainability are therefore
critical for industrial robots. An automated supervision of the robot system is
desirable as it relieves operators and can increase sram. Collision detection and
brake monitoring are examples of functionality available in commercial products
that can improve the safety and the integrity of the system. However, there are
currently little commercial solutions that allow for an automated monitoring of
the mechanical parts of the robot.
For industrial robots, the requirements on high availability are most of the times
achieved based on preventive and corrective maintenance policies. Service routines are typically performed on-site, with a service engineer. Service actions are
based on specific on-site tests or simply from a pre-determined schedule. The
later is scheduled based on the estimated lifespan of components, with considerable margins. Such maintenance solutions can deliver high availability, reducing
downtimes. The drawbacks are the high costs due to on-site inspections by an
expert and/or due to unnecessary maintenance actions that might take place.
In the current scenario, the serviceability of industrial robots can be greatly improved with the use of methods to infer the system condition and determine imminence of a critical degradation, allowing for cbm. There are however require-
1.2 Research Goals and Approach
3
ments from both the robot user and the service contractor.
The robot user seeks for improved sram. Therefore, the solution should be reliable and accurate, with minimal intervention with the operation of the
system.
The service contractor seeks for reduced service costs. Therefore, a remote and
automated solution, with no extra sensors would be desirable.
Achieving these compromises is a challenging task. This is partly because some
faults are difficult to predict, or affect the operation of the system abruptly, e.g., a
wire cut or a power supply drop. These types of faults, even when detected, might
still cause damages. Therefore, with focus on avoiding failures, the interest is
limited to faults that can be diagnosed before a critical degradation takes place,
so that timely maintenance actions can be performed.
An important type of such fault is related to the wear processes in a robot joint.
Wear develops with time/usage and critical wear levels might be detected at an
early stage, allowing for cbm. The wear processes inside a robot joint cause an
eventual increase of wear debris in the lubricant. A possible solution is therefore
to monitor the iron content in the lubricant. For a typical robot setup, this type
of approach will however contradict most of the user’s and service contractor’s
requirements.
An important characteristic of wear is that it affects friction in the robot joint. An
alternative solution, explored in this work, is thus to monitor friction changes
to infer about wear. Since the friction torques must be overcome by the motor
torques during its operation, it is possible to extract information about friction
from available signals. Friction is however dependent on other factors than wear.
In fact, friction changes caused by, e.g., temperature are typically at least as significant as those caused by wear.
1.2 Research Goals and Approach
The main objectives of this work can be explicit as follows.
Design and investigate the applicability of methods to detect critical
changes of wear based on standard sensory information and limited
intervention with the system operation to support service.
The approach to the problem can be described by the following tasks.
Extensive studies of friction. Because friction and wear are related, the problem
is initially approached by an extensive experimental study of friction in
robot joints in order to determine how critical changes of wear may affect
the system and the available data.
At this stage, it is identified that the effects of wear to friction are comparable to those caused by temperature and load, which are not measured and
can considerably vary in practice. To allow for a more extensive evaluation
1 Introduction
4
of the proposed methods for wear monitoring as well as to be used in their
design, a friction model is developed that can describe the effects of speed,
load, temperature and wear.
Design of methods for wear monitoring. The developed friction model is used
to define an approach to wear monitoring based on the estimation of a wear
related quantity. Aiming at increasing the portfolio of possible service offerings, an alternative method for wear monitoring is also suggested that does
not require knowledge of a friction model and is only based on available
data.
Extensive evaluation of monitoring methods. To verify the applicability of the
proposed methods, they are evaluated under realistic scenarios based on
real data and extensive simulations. In particular, a framework for simulation based evaluation and comparison of different solutions is proposed
which can be used to reveal important properties of the problem at hand
and of candidate solutions.
Design of methods for the detection of changes. A tool for an automated determination of fault presence is also devised which can provide an estimate of
the decision errors, supporting service decisions at a higher level.
This work is in the overlap of three main research areas, namely: industrial
robotics, tribology and fault diagnosis. To consider a problem in their intersection will require understanding of the available techniques from each of these
fields. Therefore, much of this thesis is dedicated to provide an overview of these
research areas. This will help to motivate the research presented and to identify
needs for innovative solutions. The outline of the thesis and the main contributions are described next.
1.3 Thesis Outline
The thesis is divided into two parts. Part I gives an overview of the related research areas and provides a background to the research contributions. The research contributions are presented in Part II, which contains edited versions of
published papers.
The outline for Part I is summarized below.
Chapter 2 provides an introduction to industrial robotics. The purpose is to provide an overview of important aspects of the application, the main limitations and challenges.
Chapter 3 focuses on describing friction and wear phenomena in industrial robot
joints. It provides an overview of the friction and wear processes, and of
some of the challenges behind the research goals of this work.
Chapter 4 provides an overview of fault diagnosis. It includes a description of
the different tasks in fault diagnosis and the existing compromises in their
1.3 Thesis Outline
5
design. Examples are given to provide an overview of different methods for
monitoring wear in a robot joint.
Chapter 5 presents a summary of the thesis, conclusions and recommendations
to future work.
Each chapter in Part I is concluded by presenting connections to the research
papers of Part II. A summary of the main research contributions of Part II is
given below.
Extensive studies of friction in a robot joint are presented in Papers A and B.
The effects of joint angle, load torques, temperature and wear are analyzed
through empirical studies.
Friction modeling, the effects of load torques and temperature to friction in a
robot joint are modeled and identified in Paper A.
Wear modeling, the effects of wear to friction in a robot joint are also modeled
and identified in Paper B.
Wear identification. In Paper B, a solution for wear monitoring is proposed based
on the identification of a wear related quantity from friction data.
Diagnosis of repetitive systems. Data-driven methods suitable for repetitive processes are suggested and verified experimentally in Paper C.
Evaluation of methods for scenarios of practical relevance are presented in Papers B and C. A simulation based framework for the evaluation of fault
detection algorithms is also suggested in Paper D in a general setup.
Anomaly detection, in Paper E, data-driven methods are proposed for anomaly
detection that only require availability of a nominal dataset and minimal
/ meaningful specifications from the user. Estimates of the decision uncertainties are also given which can support service decisions at a higher level.
1.3.1 Background on the publications
Edited versions of the following papers are included in Part II of this thesis. The
background for the research contributions in each paper is discussed next.
Paper A: Friction in a Robot Joint – Modeling and Identification of Load and
Temperature Effects
A. C. Bittencourt and S. Gunnarsson. Static friction in a robot joint—
Modeling and identification of load and temperature effects. Journal
of Dynamic Systems, Measurement, and Control, 134(5), July 2012.
Several reports can be found in the literature regarding the dependency of friction in a robot joint to other factors than speed, e.g., Gogoussis and Donath (1988);
Waiboer et al. (2005); Hamon et al. (2010). However, to the best of the author’s
knowledge, no detailed empirical studies of these effects in a robot joint had been
previously published.
6
1 Introduction
This work provides a deeper understanding of these phenomena based on experiments that were carried out during the summer of 2009 at abb. The main motivation for the studies was to gather understanding of these phenomena. This
would serve as a pre-requisite to the development of wear monitoring methods
based on analysis of the friction behavior. As a result, a model that can explain
the effects of temperature and load to friction was developed and validated. The
developed model is important not only for the design and validation of diagnosis
methods but also for control and simulation.
Paper B: Modeling and Experiment Design for Identification of Wear in a Robot
Joint Under Load and Temperature Uncertainties
A. C. Bittencourt and P. Axelsson. Modeling and experiment design
for identification of wear in a robot joint under load and temperature uncertainties based on friction data. IEEE/ASME Transactions
on Mechatronics, 19(5):1694–1706, October 2014.
Different approaches had been previously proposed for monitoring of friction
changes based on parameters estimated from a friction model. However, no report could be found that considered the effects of wear changes explicitly. Moreover, no detailed studies of the undesired effects of disturbances caused by temperature and load to friction were found. This is partly because there were no
available models to explain these phenomena. Another important aspect is that
performing experiments for wear monitoring is a very time consuming and expensive task.
Based on accelerated wear experiments performed in cooperation with abb, the
effects of wear to friction were studied and a model to explain the effects of wear
to friction was developed. This model, combined with the model of Paper A, is
very important for the design and evaluation of solutions for wear diagnosis and
are used extensively through Part II. In this paper, the models are used in the
proposed method for the estimation of a wear related quantity. As it is shown,
a careful experiment design can lead to reliable estimates of the wear quantity,
despite the presence of disturbances and modeling uncertainties.
Paper C: A Data-driven Approach to Diagnostics of Repetitive Processes in the
Distribution Domain
A. C. Bittencourt, K. Saarinen, S. Sander-Tavallaey, S. Gunnarsson,
and M. Norrlöf. A data-driven approach to diagnostics of repetitive
processes in the distribution domain – Applications to gearbox diagnostics in industrial robots and rotating machines. Mechatronics, -(0):
–, 2014. available online.
A repetitive operation is found in various applications, e.g., in automated manufacturing. Repetition can also be forced with the execution of specific diagnostic
routines but with the drawback of reduced availability. The repetitive execution
of a system provides redundancies about the system’s behavior which are directly
found in the data. For example, it is possible to compare the results of the exe-
1.3 Thesis Outline
7
cution of a diagnostics routine performed today to how it is performed in a year.
The differences in the results can relate the system’s deterioration over the period. The ideas behind the methods emerged via a combination of development
and testing of methods in collaboration with abb.
The methods were developed with the interest focused on diagnosis of industrial
robots, where a repetitive operation is commonly found and repetitive data can
thus be found during normal operation. As shown in the paper, with little design requirements, the proposed methods can be used to monitor wear changes
despite presence of disturbances. Applicability to other types of mechanical systems is also studied based on vibration data.
Paper D: Simulation based Evaluation of Fault Detection Algorithms
A. Samuelsson, A. C. Bittencourt, K. Saarinen, S. S. Tavallaey, M. Norrlöf, H. Andersson, and S. Gunnarsson. Simulation based evaluation
of fault detection algorithms with applications to wear diagnosis in
manipulators. In Proceedings of the 19th IFAC World Congress, Cape
Town, South Africa, 2014.
Before deployment of fault detection solutions, it is important to study the behavior of the methods in practical scenarios. The evaluation of wear monitoring
methods based on field or laboratory studies is time and cost critical and the use
of simulations is a more viable alternative.
This paper aims at providing a framework for the evaluation and comparison
of fault detection algorithms. Simulation based approaches are proposed in an
attempt to determine which disturbances affect a given method the most, how
to compare different methods and how to determine the combination of disturbances and faults effects where the methods perform satisfactorily.
This work was motivated by the needs at abb of a framework to evaluate different available methods for wear monitoring and was partly carried out during
Andreas Samuelsson’s Master thesis,
A. Samuelsson. Simulation based Evaluation of Mechanical Condition
Change Methods. MSc. thesis LiTH-ISY-EX-11/4575-SE, Department
of Electrical Engineering, Linköping University, Linköping, Sweden,
2012.
Paper E: Data-Driven Anomaly Detection based on a Bias Change
A. C. Bittencourt and T. Schön. Data-driven anomaly detection based
on a bias change. In Proceedings of the 19th IFAC World Congress,
Cape Town, South Africa, 2014.
In order to decide for the presence of a critical condition, a decision rule, e.g., a
threshold check, is needed. Optimal decision rules are possible that minimize
the probabilities of making incorrect decisions, i.e., likelihood ratio tests. The
optimal decision rule requires availability of statistical models for the quantity
being tested in both normal and abnormal conditions. Often, such statistical
1 Introduction
8
models are not available, in particular for the abnormal case, and approximations
or assumptions are introduced to devise a decision rule.
In this paper, a data-driven method is proposed to find an approximate test that
only requires availability of nominal data and specification of a desired error
probability. It is based on the assumption that an abnormality will appear as a
bias change relative to nominal, which is rather intuitive. The advantages lie in
the flexibility of the approach, minimal specification requirements from the user
and the possibility to provide estimates of the decision errors.
1.3.2 Relevant and additional work
The author was introduced to the wear monitoring problem already in 2007 during a Master Thesis project carried out at abb,
A. C. Bittencourt. Friction Change Detection in Industrial Robot Arms.
MSc. thesis XR-EE-RT 2007:026. Department of Electrical Engineering, The Royal Instute of Technology (KTH), Stockholm, Sweden, 2007.
In the contribution, a method for friction change detection was developed. The
basic idea was to monitor the changes found directly on the friction data. A testcycle was required in order to collect friction data, in a similar way as in Paper B.
The effects of load, lubricant and temperature were briefly investigated during
the work and motivated the more thorough experiments of Paper A.
The methods presented in Paper C were submitted as part of the patent application,
S. Sander-Tavallaey, K. Saarinen, H. Andersson, and A. C. Bittencourt.
Condition monitoring of an industrial robot, October 2012. URL http://
patentscope.wipo.int/search/en/WO2013050314.
Another patent application during the period of this work is,
A. Isaksson, A. C. Bittencourt, K. Forsman, and D. Peretzki. Method
for controlling an industrial process, October 2010. URL http://
patentscope.wipo.int/search/en/WO2012048734,
which describes a method to mine historical process data that can be potentially
used to identify models of dynamic systems. The method is described in the
paper,
D. Peretzki, A. J. Isaksson, A. C. Bittencourt, and K. Forsman. Data
mining of historic data for process identification. In Proceedings of
the 2011 AIChE Annual Meeting, October 2011.
This work was not included in the thesis for consistency of the presentation.
Part I
Background
2
Basics of Industrial Robotics
The International Organization for Standardization, iso, proposes the following
definitions in ISO 8373 (1994).
Definition 2.1 (iso 8373:1994 No. 2.15 – Robotics).
Robotics is the
Robotics is the practice of designing, building and applying robots.
Definition 2.2 (iso 8373:1994 No. 2.6 – Manipulating industrial robot).
A manipulating industrial robot is an automatically controlled, reprogrammable, multipurpose, manipulator programmable in three or
more axes, which may be either fixed in place or mobile for use in industrial automation applications.
Note: The robot includes the manipulator (including actuators) and
the control system (hardware and software).
The above definitions make a clear distinction of industrial robots in the manner
that they are used, i.e. “in industrial automation applications”. The first industrial robot was operating in 1961 in a General Motors automobile factory in New
Jersey. It was Devol and Engelberger’s unimate. It performed spot welding and
extracted die castings (Westerlund, 2000). Since then, many new applications of
industrial robots have been introduced, e.g. welding, cutting, forging, painting,
assembling, etc. Industrial robots penetrated quite rapidly in manufacturing and
specially in the automotive industry, which is still the largest consumer of industrial robots. In 2007, there were more than one million industrial robots in operation worldwide, reaching around 1.5 million in 2013 and with expected increase
rates for the next years (Tencer, 2013).
11
2 Basics of Industrial Robotics
12
(a) An abb irb 6 from 1973.
(b) A modern abb irb 7600.
Figure 2.1: The fives axes robot irb 6 was the first all-electrically actuated
robot controlled by a microcomputer. The six axes robot irb 7600 is suitable
for high payload applications.
Industrial robots are a key factor to improve productivity, flexibility, quality
and safety of technical systems. The history of industrial robotics development
is filled with technological milestones. In 1971, the first all-electrically actuated robot was introduced by Cincinnati Millacron, whose robotics development
team was later acquired by abb in 1990. In 1973, abb released irb 6, the first
microcomputer-controlled robot, which was also all-electrically actuated. Remarkably, this setting is still dominant for modern industrial robots, see Figure 2.1.
The mechanical structure of a standard industrial robot is composed by links
and joints. Links are the main bodies that make up the mechanism and the links
are connected by joints to each other. A joint constraints the relative motion
of the connecting links and are categorized accordingly. The configuration of
links and joints defines the kinematic chain of the robot. The number of joints
defines the number of manipulated degrees of freedom, dof, of a robot. The most
common configuration of industrial robots is the six dof with serial kinematics
and revolute joints, meaning that links are connected in series through joints
allowing for rotational movements. This type of robots are also known as “elbow”
manipulators for their resemblance with the upper arm of a human. For elbow
manipulators, the first three axes, also called main axes, are used to achieve a
desired position of the end-effector. The links of the main axes are bigger since
2.1 Actuators and Sensors
13
they drive more load compared to the last three, wrist axes, which are used to
manipulate the orientation of the end-effector.
The main developments in industrial robotics have been directly connected to its
main market, the automotive industry. This resulted in products with high cost
efficiency, reliability and performance (Brogårdh, 2007). A cost-driven development means the need of cost reduction of the components. This leads to a more
difficult control design to handle the larger variations in kinematic and dynamic
parameters, lower mechanical resonance frequencies and larger nonlinearities.
In order to meet the performance required from industrial robots, a broad understanding of the system is needed. This chapter reviews the basics of industrial
robotics.
2.1 Actuators and Sensors
An industrial robot is a complete system that interacts with its surroundings. Its
degree of autonomy is directly related to the sensory information available, the
knowledge built in the system (e.g. models/learning), and the possibilities to perform actions. Following demands on cost efficiency and reliability, the amount
and variety of sensors are remarkably small in typical applications of industrial
robots. With the development of new applications and higher demands on autonomy, alternative sensors are becoming more common (Brogårdh (2009)).
2.1.1 Basic setup
As mentioned in the beginning of this chapter, modern industrial robots are most
commonly actuated with electrical motors. The permanent magnet synchronous
motor, pmsm, is a popular choice due to its high power density, easy operation
and performance. The output torque of such motor can be divided into two parts,
• the dominant electromagnetic torque, arising from the interaction between
the rotating magnetic field and the magnet and,
• the pulsating torque, an angular dependent component composed of cogging and ripple torques (Jahns and Soong, 1996).
The pulsating torque leads to challenges in control of machines actuated with
pmsm, see, e.g., Proca et al. (2003); Mohamed and El-Saadany (2008). Furthermore, the relation between applied current and output torque varies with temperature due to a reversible demagnetization of the magnets (Sebastian (1995)).
A power amplifier is used to modulate the power used as input to the motors.
In order to provide high torques and low speeds, a gearbox transmission is used
at the motor output. The rotary vector (rv) type is a popular choice of compact
gearboxes due to their low backlash, high gear ratio (in the order of 100 − 300)
and size. This type of transmission is commonly found in the main axes of a
manipulator. In the wrist axes, also harmonic drive gears are used as well as
special gear solutions. See Figure 2.2 for examples of motor and gear units used
in industrial robots.
14
2 Basics of Industrial Robotics
Figure 2.2: An abb motor (left) and a Nabtesco rv gear unit scheme (right,
picture courtesy of Nabtesco.)
Typically, only the rotation angle of the motor shaft, electrical quantities (voltages
and currents), and winding temperature are measured. Optical encoders and
resolvers are the most commonly used sensors for the angular measurements.
The high accuracy of encoders and resolvers used allows for differentiation of the
angular measurements to provide estimates of speed and acceleration.
2.1.2 Application dependent sensors
With the basic sensors and refined models of the system, it is possible to achieve
high path and positioning performances. This allows robots to be used in applications with a controlled/predictable environment. In more demanding applications, where the workpiece and environment are changing or in contact applications, the use of alternative sensors may be needed.
Six dof force/torque sensors can be used in applications such as high precision
assembly of drive trains. This type of sensor is also important in machining applications, such as grinding and polishing, see e.g. Jonsson et al. (2013). The
use of high speed cameras combined with image processing algorithms is also
important in pick and place applications. Applications demanding very high accuracy might require the use of additional sensors on the arm side of the robot.
Measurements of the arm variables help to reduce the influence of backlash and
compliance of the gears on the accuracy of the robot. This can be achieved, e.g.,
with the use of encoders, torque sensors and inertial measurement units, imu’s,
in the actuator transmissions and the arm system. For a review, see Brogårdh
(2009); for an example on the use of imu’s to improve accuracy, see Axelsson
(2014).
Remark 2.1. While the use of additional sensors can increase the robot autonomy, performance and safety, it also means higher costs.
2.2 Modeling
15
ϕ2
p1
ϕ3
p2
p3
ϕ1
p0
Figure 2.3: Joint positions, ϕi , and coordinate frames, p i−1 , for an elbow
manipulator with joints i ∈ {1, 2, 3}. The end-effector is fixed at frame p3 .
2.2 Modeling
Given the limited sensory information from the measurements of the angles of
the motor shafts, the high demands on accuracy and performance expected from
industrial robots are only possible with the use of reliable models and modelbased control (Brogårdh (2009)). Models are also important for design, simulation, diagnosis, etc. They play a significant role in all industrial robotics.
In this section, modeling of industrial manipulators is reviewed. The presentation follows standard textbooks, see e.g. Sciavicco and Siciliano (2000) and
Spong et al. (2006).
2.2.1 Kinematics
The kinematics describes the motion without considering the forces and torques
causing it. A kinematic model only depends on the geometric description of the
robot. Let ϕi be the ith joint position at the arm side and p i−1 be a frame defined
at that joint. For a configuration with n joints, there are n+1 frames where the
end-effector is considered fixed at frame p n . See Figure 2.3 for an illustration.
By using a coordinate transformation, it is possible to describe a point attached
to coordinate frame i in the coordinate frame i −1 by
i−1
p i−1 = Ri−1
i p i + di
(2.1)
where Rii−1 and dii−1 are a rotation and a translation from frame i to frame i −1
respectively. The above transformation can be written as a homogeneous transformation
"
# " i−1
#
p i−1
Ri
dii−1
pi−1 ,
=
pi ,
(2.2)
1
0
1
|
{z
}
,Hii−1
2 Basics of Industrial Robotics
16
which facilitates calculations since consecutive frame transformations simplify to
multiplications of matrices. Notice that the homogeneous transformation Hii−1 is
a function of ϕi and of the links’ geometry.
Forward kinematics
The forward kinematics is the problem of finding the end-effector pose x (position and orientation) relative to the base frame given the joint variables ϕ. This
can be achieved with the use of a homogeneous transformation from the tool
pose to the base frame. For a configuration with n joints, the transformation is
described as
" 0
#
Rn (ϕ) dn0 (ϕ)
0
Hn (ϕ) =
,
(2.3)
0
1
from which it is possible to extract the pose, x, of the end-effector. The DenavitHartenberg convention provides a manner to choose the reference frames that
allows for a systematic analysis. For a serial robot, the direct kinematics always
has a unique solution.
Taking the time derivative of the end effector pose, gives a relation between the
joint velocities ϕ̇ and the linear and angular velocities of the end-effector as
ẋ = J (ϕ)ϕ̇,
(2.4)
where J (ϕ) is known as the analytical Jacobian matrix. The accelerations can be
found by taking the time derivative again, yielding
!
d
(2.5)
ẍ = J (ϕ)ϕ̈ +
J (ϕ) ϕ̇.
dt
The Jacobian matrix is an important quantity in robotics, it can be used to find
singular configurations, transformation of tool forces to joint torques, etc.
Inverse kinematics
The reverse problem, finding the joint positions ϕ given the end-effector pose is
known as the inverse kinematics. The inverse kinematics problem is important
for trajectory generation, when a desired tool path needs to be transformed to
joint positions. For the serial robot, it can be expressed as solving the nonlinear
equations
Hn0 (ϕ) = H10 (ϕ1 )H21 (ϕ2 ) · · · Hnn−1 (ϕn ) = H
(2.6)
for a given right-hand side H, where ϕi is ith joint position and Hii−1 is given
by (2.2). An analytical solution is not always possible, in which case a numerical
solver can be used, and even if a solution exists it is typically not unique.
2.2.2 Dynamics
A dynamic model describes the relation between the robot motion and the forces
and torques that cause it. Dynamic models are important for simulation, trajectory generation and control. In feed-forward control, the motor torques required
2.2 Modeling
17
to achieve a certain path are computed from the inverse dynamics.
The simplest modeling approach is to consider all links as rigid bodies. From this
simplification, there are different possible methods to derive rigid multi-body
models. The Euler-Lagrange formulation considers the Lagragian equation
L(ϕ, ϕ̇) = K(ϕ, ϕ̇) − P (ϕ),
(2.7)
where the Lagrangian L(ϕ, ϕ̇) is defined as the difference between kinetic, K(ϕ, ϕ̇),
and potential energies, P (ϕ). The equations of motion are given from the EulerLagrange equations
∂
d ∂
L(ϕ, ϕ̇) −
L(ϕ, ϕ̇) = τi ,
dt ∂ϕ̇i
∂ϕi
for i = 1, . . . , n
(2.8)
where τi is the applied torque at joint i. By writing the kinetic energy as a
quadratic function K(ϕ, ϕ̇) = 12 ϕ̇ T M(ϕ)ϕ̇, where M(ϕ) is the total inertia matrix,
g
∂
P (ϕ) into the vector τ g (ϕ)
gathering gravitational terms of the form τi (ϕ) = ∂ϕ
i
and terms involving ϕ̇i2 and cross-products of ϕ̇i ϕ̇j in C(ϕ, ϕ̇), the resulting rigid
multi-body model is of the form
M(ϕ)ϕ̈ + C(ϕ, ϕ̇)ϕ̇ + τ g (ϕ) = τ
(2.9)
where τ is the vector of applied torques. This model can be extended by including
a dissipative friction term, τ f , which is typically modeled as a nonlinear function
of ϕ̇, see Chapter 3 for more on friction.
Including flexibilities
In most cases when modeling robots, a rigid multi-body model is not sufficient
to describe the system in a realistic manner. The approximation of a rigid gearbox is specially unrealistic for compact gearboxes. Also, with a trend of lighter
robots, the flexibilities of bearings- and links are also becoming significant. The
model for a flexible robot structure can, as a first approximation, be described by
lumped masses connected by springs and dampers.
For instance, a flexible joint model can be achieved by modeling the joint as a
system with two masses connected by a torsional spring-damper, as shown in
Figure 2.4. Neglecting possible inertial couplings between motor and armi , the
resulting model can be described as
τ a = M a (ϕ a )ϕ̈ a + C(ϕ a , ϕ̇ a ) + τ g (ϕ a ) + τ f ,a (ϕ̇ a )
τ a = K(Λϕ m − ϕ a ) + D(Λ ϕ̇ m − ϕ̇ a )
τ m − Λτ a = M m ϕ̈ m + τ f ,m (ϕ̇ m )
(2.10)
(2.11)
(2.12)
where the superscripts · a and · m relate to variables at the arm and motor sides
respectively, Λ is the inverse gear ratio matrix, K and D are the stiffness and
damping matrices. The friction torque is here divided between the motor and
arm side, τ f ,m (ϕ̇ m ) and τ f ,a (ϕ̇ a ) respectively. Friction occurs at different compoi According to Spong (1987) this is a reasonable approximation if the transmission ratio is large.
2 Basics of Industrial Robotics
18
nents in the gearbox, at different gear ratios, meaning different reductions when
seen at the motor side. See, e.g. Moberg (2010), for a detailed treatment on modeling of flexible robots.
ϕ3m
ϕ2a
p3
ϕ2m
ϕ3a
ϕ1a
ϕ1m
Figure 2.4: Illustration of a flexible robot structure where the flexibilities are
modeled as lumped masses connected by springs and dampers.
2.3 Identification
The described models depend on a number of parameters that are most often unknown or partly known. In order to make use of models, e.g. for control and
simulation, the modeling process can be complemented with identification procedures. Identification is used to find and verify the parametric description of
the models from experiments. As introduced in the previous section, the different models can relate to kinematics, dynamics and joint -related phenomena. A
summary of these identification problems is given below.
Kinematic models are important for positioning of the end-effector. The parameters in the model relate to the geometric description of the kinematic chain. These
parameters can be partly obtained during the design process, e.g. available from
cad models. There are however errors that could relate, amongst other sources,
to tolerances during production and assembly of the robot. An identification
procedure can be used to correct for these errors, considerably improving the volumetric accuracy of the robot. The process of identifying these parameters is also
known as kinematic calibration or robot calibration, and requires measurements
of the end-effector position. For a survey on the topic, see Hollerbach (1989).
Dynamic models are important for simulation and feed-forward motion control
of robots. The identification of dynamic models of robots is a much studied problem and several approaches can be found, see Wu et al. (2010) for an overview.
An important consideration is the type of dynamic model considered. Rigid
multi-body models are typically parametrized as a function which is linear in
2.4 Reference Generation and Control
19
the parameters. For example, the model in (2.9) can be rewritten as a linear regression
τ = Φ(ϕ, ϕ̇, ϕ̈)θ,
(2.13)
where Φ( · ) is a matrix regressor function, dependent on ϕ and its derivatives,
and θ are the rigid-body parameters. Based on data from an identification experiment, the parameters θ can be found, e.g., based on a weighted least squares
minimization
−1
b = arg min τ − Φθ T W τ − Φθ = ΦT WΦ ΦT Wτ,
θ
(2.14)
θ
where τ and Φ are the stacked torque and regressors achieved from the identification experiment. The choice of weight matrix W will affect the solution and
different criteria are possible, see, e.g., Gautier and Poignet (2001); Swevers et al.
(1997). Finally, the trajectory must be chosen carefully to avoid excitation of
flexible modes and improve the estimation performance. Identification of parameters describing the flexibilities is a more involving problem since only a subset
of the states can be measured and a linear regression cannot be formed. These
models are however important for improved performance of robot control. For
a detailed treatment on identification of dynamic models and flexibilities, see
Wernholt (2007); Moberg (2010); Wernholt and Moberg (2011).
Joint models. Due to the complex construction of a robot joint, its characteristics
are often uncertain and nonlinear phenomena are common. Nonlinearities that
can be of significant influence in a robot joint are related to friction, backlash and
nonlinear stiffness. Available parametric models are often achieved from empirical modeling for a specific platform since it is difficult to predict the characteristics of these nonlinearities in general. For example, the amount of backlash and
friction will depend on how the joints were assembled. Therefore, these models
are most often found from an experimental identification procedure. It is important to notice that the identification of dynamic models is facilitated if an accurate joint model is available. For example, in Wernholt (2007) it is reported that
the friction at low speeds makes it difficult to identify the resonances related to
a flexibility. This is because friction adds damping to the system. With a known
friction model, its effects can be analytically removed from the data, making the
identification of dynamic parameters more reliable.
2.4 Reference Generation and Control
From the perspective of a robot user, it is convenient to be able to program the
robot in a high level of abstraction. Typically, objectives can be defined in the task
space, and the user does not need to worry about how each joint is controlled.
A robot manufacturer dependent programming language is used where instructions to the robot can be given in task (or joint) space. This can be done manually
by typing the code or in some cases by demonstration. This process can also be
partly automated with the use of cad/cam softwares allowing greater flexibility.
2 Basics of Industrial Robotics
20
An example of a robot task program is given in Algorithm 1. In order to perform
a task, different problems must be solved.
Algorithm 1 My spot-welding task.
Move to point A0 as fast as possible.
Approach point A1 slowly.
Perform a spot weld.
Move to point B0 as fast as possible.
...
Motion planing. First, given a task, e.g. the one defined in Algorithm 1, a path
to be executed by the robot must be generated. This is made by a motion planner, which calculates the movements that the robot must make. At first, the
programmed movements are interpreted with respect to what geometry that the
path will have (line, circle, spline etc.) and then the path is interpolated to consist
of discrete steps, which are transformed from task space to joint space using the
inverse kinematic model.
Trajectory generation. The time dependence of the robot movements, i.e. a trajectory, can be calculated either in the task space or in the joint space. Finding
a trajectory involves optimization of the use of the dynamic capabilities of the
robot with respect to speed- and acceleration performance. Let ℧ denote a trajectory, the trajectory generation is essentially an optimization problem including,
℧r = arg min
℧
Objective(℧)
subject to Path(℧)
Dynamics(℧)
Mechanical limitations(℧)
where the solution, ℧r , is used in the next stage as a reference for the motion
control. The objective can be, e.g., minimal cycle-time or minimal energy. The
constraints ensure that the trajectory runs through the path according to the dynamics of the manipulator and avoiding mechanical limitations such as motor
position and speed ranges, maximum allowed forces and torques in the joints, etc.
Notice that the solution for this optimization problem can considerably affect the
time and performance of the task execution and is highly dependent on the models used. For example, in Ardeshiri et al. (2011) the inclusion of speed dependent
constraints in a convex formulation of the problem allowed for reductions of the
path tracking time by 5−20%. Speed-dependent constraints are motivated from
physical modeling of the motors and the drive system, they can, e.g., relate to
viscous friction.
Motion Control. Finally, when the reference trajectory is generated, it is possible to execute the task with the help of the servo control. Important features of
the servo are trajectory tracking, robustness and disturbance rejection. Different
control strategies and structures are possible depending on the sensors available,
2.4 Reference Generation and Control
21
τffw,m
℧r
Inverse
Dynamic
Model
ϕ r,m
ϕ̇ r,m
−
Controller
+
τ
Motor
Model
i r,m
−
Current
Controller
Motors
ϕm
ϕ̇ m
Gears
ϕa
Robot
Arm
x
im
Figure 2.5: A model-based control scheme for trajectory tracking. A feedforward action τ ffw,m and motor references ϕ r,m , ϕ̇ r,m for the outer feedback
loop are computed based on the reference trajectory ℧r using an inverse dynamic model. An inner control loop is used to control the motor current
according to i r,m which is achieved from a desired input torque vector τ using a motor model.
controlled variables, etc., see Moberg (2010); Brogårdh (2009) and available textbooks for details. Here, a common control approach is discussed for the typical
setup, with measurements only at the motor side.
Model-based control for trajectory tracking
An overview of one possible robot control scheme is given in Figure 2.5. The
desired trajectory ℧r contains the joint information through time at the arm side,
that is, ϕ r,a and its derivatives. With angular position measurements available at
the motor side, ϕ m , and an estimate of ϕ̇ m achieved from differentiation, the arm
side references are transformed to the motor side, yielding ϕ r,m , ϕ̇ r,m which are
used in the outer feedback control loop.
To improve performance, an inverse dynamic model is used to generate feedforward motor torques, τ ffw,m . The input torque vector τ is the total torque the
motor should generate to drive the robot in the desired manner and is composed
of both feed-forward and feedback actions. Since the motor torque is not measured, a motor model is used to transform τ to a current reference, i r,m , for the
inner current control loop. The motor variables ϕ m and ϕ̇ m are fed back to the
outer control loop. At the output is the end-effector pose x.
The inner current control loop has much faster dynamics than the outer loop.
When designing the outer loop, it is therefore common to accept a constant relation between the measured motor currents and the motor torques, that is τ = K i m .
As pointed out in Section 2.1.1, this relation actually varies with temperature
since the nominal performance of the motors degrades with increased temperature.
22
2 Basics of Industrial Robotics
2.5 Summary and Connections
This chapter provided an overview of important aspects to consider when working with industrial robots, the problems, technologies and limitations. Two aspects are particular about the development of industrial robots, the limited sensory information available and the importance of using different types of robot
models. The purpose of this introduction has also been to provide a background
to the research results presented in the second part of this thesis.
In Papers A and B, an experimental procedure is described for the estimation of
constant-speed friction levels in a robot joint. During the procedure, the torque
reference to the inner current control loop, recall Figure 2.5, is used as an estimate of the actual applied torques. The approximation of an ideal current loop,
giving τ = K i m , is thus important. The gain K should not be speed dependent and
the temperature dependence of K should be small. Based on an experimental investigation of the phenomenon for motors of similar types as the ones considered
in thesis, Tenerz (2011) shows that variations caused by temperature may be as
large as 5%. These dependencies on temperature are therefore neglected during
the studies presented in this thesis.
3
Joint Friction and Wear
Friction exists in all mechanisms to some extent. It can be defined as the tangential reaction force between two surfaces in contact. There are different types of
friction, e.g. dry friction, viscous friction, lubricated friction, skin friction, internal friction. Friction is not a fundamental force but the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Due to its
complex nature, it is often difficult to described it from physical principles.
One reason for the interest in friction in the joints of a manipulator is the need
to model friction for control purposes. A precise friction model can considerably
improve the overall performance of a manipulator with respect to accuracy and
control stability, see e.g., Olsson et al. (1998); Bona and Indri (2005); Guo et al.
(2008); Susanto et al. (2008); Kim et al. (2009). Since friction can relate to the
wear processes of mechanical systems (Blau, 2009), including robot joints, there is
also interest in friction modeling for fault detection, see, e.g., Freyermuth (1991);
Vemuri and Polycarpou (2004); McIntyre et al. (2005); Mattone and Luca (2009);
Brambilla et al. (2008); Caccavale et al. (2009); Namvar and Aghili (2009).
In a robot joint, with several components interacting such as gears, bearings, and
shafts, which are rotating/sliding at different velocities and under different lubrication levels, it is difficult to separate and model friction at a component level.
A typical approach is to consider these effects collectively, as a “lumped” joint
friction. For examples of friction models at a component level, see SKF (2011).
Friction opposes motion, dissipating energy. A part of the work produced by friction appears as heat transfer, vibrations and acoustic emissions. Other outcomes
of friction are plastic deformation, adhesion and fracture, see e.g. Bryant (2009).
The latter outcomes can relate to wear, which is defined as “the progressive loss of
material from the operating surface of a body occurring as a result of relative mo23
3 Joint Friction and Wear
τf
24
0.14
0.12
0.1
0.08
0.06
bl
ml
BL
0
50
ML
100
150
200
ϕ̇ [rad/s]
EHL
250
ehl
Figure 3.1: Friction curve for constant speed movements and the lubrication
regimes illustrated at contact level.
tion at its surface” (Lansdown et al., 1987). The need for relative motion between
surfaces implies that wear is related to the mechanical action between surfaces.
This is an important distinction to other processes with a similar outcome and
very different nature, e.g. corrosion and cavitation.
3.1 Basics of Tribology
Tribology is the study of the phenomena taking part in the interaction of surfaces in relative motion, including friction, wear and lubrication. The most important friction characteristics for control applications are usually described by
a so-called friction curve, which is a plot of friction levels as function of speedi .
An example of such plot achieved from experiments in a robot joint can be seen
in Figure 3.1ii,iii . The nonlinear behavior from low to high speeds is typical in
lubricated friction and is known as the Stribeck effect (Woydt and Wäsche, 2010;
Jacobson, 2003). This behavior is present in a robot joint due to the presence of
lubricant in the gearbox and motor shaft. Notice that the friction in the motor
is dry. The use of lubricant is essential to decrease the wear processes. It acts as
a separation layer between the surfaces. With the use of additives, e.g. Extreme
Pressure (ep) additives, it can even create a chemical barrier between the surfaces
under high contact pressures, reducing low speed friction and wear.
The friction curve is divided in three regions according to the lubrication regime:
boundary lubrication (bl), mixed lubrication (ml) and elasto-hydrodynamic lubrication (ehl). The phenomenon present at very low speeds (bl) is mostly related to interactions between the asperities of the surfaces in contact. With the
i As presented originally by Stribeck (1902), a friction curve is plotted as a function of speed normalized by the ratio of normal load and lubricant viscosity. For simplicity however, it is many times
shown only as a function of speed.
ii In the figure, the friction torques are normalized to the maximum allowed torque to the joint and
are displayed as dimensionless quantities, this convention is followed in the whole thesis.
iii This type of curve is obtained when the speed levels are stable and include no transient phenomena. There are also dynamic effects related to friction, see Section 3.3.
3.2 Friction Dependencies in Robot joints
25
increase of velocity, there is a consequent increase of the lubricant layer between
the surfaces with a decrease of contact friction (ml). The decrease of contact friction continues until it reaches a full lubrication profile (ehl), with a separation of
the surfaces by the lubricant. In ehl, friction is proportional to the force needed
to shear the lubrication layer, and it is thus dependent on the lubricant properties
(e.g. viscosity).
The wear processes are most significant in bl and ml, where contact friction is
significant. In a full-film lubrication, there is theoretically no wear taking place,
but it still happens because of eventual breakdowns of this layer. It is important
to notice that due to the high gear ratio of the gearboxes used in industrial robots,
the components closer to the output will be moving slower in comparison to the
ones closer to the input. Therefore, at a component level, wear might occur even
in the ehl region of the joint friction curve.
3.2 Friction Dependencies in Robot joints
At a contact level, friction is dependent on the contact geometry, topology, properties of the materials, position, relative velocity, force/torque levels, temperature,
lubricant, etc. (Al-Bender and Swevers, 2008). Depending on the setup, each of
these factors will be more or less significant to the total friction.
In robot joints, the friction dependencies will differ depending on the size and
type of joint considered. For elbow manipulators, the main axes undertake significant load levels and the wear processes in these axes are usually more significant
than in the wrist axes. This thesis focuses on the study of friction and wear in the
main axes of large robots, equipped with rv gearboxes, recall Section 2.1.1. The
dependencies of friction for such joints have been studied based on experiments,
the effects of the most relevant variables to the friction curve are shown in Figure 3.2. It should be noted that, except for Figures 3.2a and 3.2b, the curves are
obtained for different robots. The effects are summarized below.
Load. The effects of load follow from the consequent increase of contact pressure
between the surfaces in contact. It leads to a generalized increase of the friction
curve, with a more significant increase at very low speeds, i.e. in the bl regime.
Lubricant. In lubricated mechanisms, both the thickness of the lubricant layer
and its viscosity play an important role in the resulting friction properties. The
higher viscosity leads to higher shear forces and therefore higher friction levels
in the ehl regime.
Temperature. The viscosity is also dependent on the temperature of the lubricant
(Seeton, 2006), the higher the temperature, the lower the viscosity. This can be
observed in Figure 3.2b with a decrease of friction in the ehl regime at higher
temperatures. The effects of temperature are however more complex, changing
also the bl and ml regimes. A possible explanation is that temperature also
considerably affects the interaction forces of the surfaces in contact. This could
be caused, e.g., by an asymmetric dilation of the gearbox components.
3 Joint Friction and Wear
26
0.16
0.14
0.2
0.4
0.6
0.8
0.14
1
0.12
0.12
τf
τf
0.1
0.08
60
70
80
0.1
0.08
0.06
0.06
0.04
0
0.04
0
50 100 150 200 250
ϕ̇ [rad/s]
(a) Normalized load torques. A generalized bias-like increase with pronounced
increases in the bl regime.
0.20
0.18
0.15
0.12
0.09
0.06
0.03
50 100 150 200 250
ϕ̇ [rad/s]
(b) Temperature (C◦ ). Significant increases in the bl and ml regions and decreases in the ehl region.
Increased backlash
Normal variation
0.20
0.18
0.15
0.12
0.09
0.06
0.03
τf
τf
50
40
µ = 150
µ = 220
µ = 320
0
100
200
ϕ̇ [rad/s]
τf
(c) Gearbox lubricant. Increases in the
ehl region with kinematic viscosity. The
viscosity values are in mm2 /s and at 40
C◦ .
0.12 0
0.1
0.08
0.06
0.04
0.02
0
20
50
0
300
40
100
200
ϕ̇ [rad/s]
300
(d) Backlash for different robot individuals. Decreases in the ml and ehl regions.
60
150
100
ϕ̇ [rad/s]
80
200
100
250
(e) Wear. Increases concentrated in the ml region followed by
a generalized increase. The colormap relates to the length of
accelerated wear tests.
Figure 3.2: Effects of different factors to the friction curve.
3.3 Modeling
27
Wear. The increase of friction with wear as seen in Figure 3.2e is related to,
amongst others, the accumulation of wear debris in the circulant lubricant. At
early stages, the changes are observed specially in the ml regime, followed by
generalized increases.
Backlash. The decrease of friction with backlash seen in Figure 3.2d can possibly be explained by a consequent loosening of the gearbox components, yielding
lower contact pressures. Notice that backlash might follow from a degenerate
wear process, where the amount of material removed by wear starts to be significant enough to create undesired clearances between the surfaces.
3.3 Modeling
Due to the complex nature of friction in a robot joint, it is common to accept
models based on empirical observations of the phenomena. The history of the
development of empirical friction models is extensive, see e.g. Dowson (1998).
At a contact level, the surfaces’ asperities can be compared to bristles on a brush.
Each of these (stiff) bristles can be seen as a body with its own dynamics which are
connected by a similar bulk. Different models have been proposed to model this
dynamic behavior of friction, and some examples are presented in Harnoy et al.
(2008); Al-Bender and Swevers (2008); Åström and Canudas-de Wit (2008). A
typical approach is to consider all the dynamics into a single state (Dupont et al.,
2002).
The LuGre fricion model, Olsson et al. (1998), is a common choice of dynamic
models in robotics. For a revolute joint, the friction torque is given by the LuGre
model as
τ f = σ0 z + σ1 ż + h(ϕ̇)
|ϕ̇|
ż = ϕ̇ − σ0
z,
g(ϕ̇)
(3.1a)
(3.1b)
where the state z captures the average dynamic behavior of the asperities. It can
be interpreted as their average deflection, with stiffness σ0 and damping σ1 .
Since z is not measurable, it is difficult to estimate the parameters describing the
dynamic behavior of friction, i.e. [σ0 , σ1 ]. In practice, it is common to accept
only a static description of (3.1). In steady-state, (3.1) is equivalent to the static
model:
τ f (ϕ̇) = g(ϕ̇)sign(ϕ̇) + h(ϕ̇)
(3.2)
which is fully described by the g- and h functions. In fact, (3.1) simply adds
dynamics to (3.2).
The function h(ϕ̇) represents friction in the ehl regime, where friction has a
velocity strengthening behavior. For Newtonian fluids this behavior is directly
3 Joint Friction and Wear
28
8
τf
7
6 ϕ<0
5
4
static
A = 10−2
A = 10−1
6
8
×10−3
ϕ>0
0
2
4
ϕ̇
Figure 3.3: Simulation of a LuGre model under different acceleration levels
A and the related static friction model. The parameters are chosen for illustrative purposes with static parameters [f c , f s , f v , ϕ̇s , α] = [2, 5, 8 102 , 1 10−3 , 2]
and dynamic parameters [σ0 , σ1 ] = [1.4 106 , 2.42 103 ].
proportional to speed, yielding the relationship
h(ϕ̇) = f v ϕ̇
(3.3)
for the viscous behavior of friction. The function g(ϕ̇) captures the bl and ml
regimes, where friction has a velocity weakening behavior. Motivated by the
observations mainly attributed to Stribeck (Jacobson, 2003; Woydt and Wäsche,
2010; Bo and Pavelescu, 1982), g(ϕ̇) is usually modeled as
g(ϕ̇) = f c + f s e
α
ϕ̇
− ϕ̇ s
,
(3.4)
where f c is the Coulomb friction, f s is defined as the standstill friction parameteri ,
ϕ̇s is the Stribeck velocity, and α is the exponent of the Stribeck nonlinearity. The
resulting static friction model is given by
α #
"
ϕ̇
− ϕ̇ τf (ϕ̇) = f c + f s e s sign(ϕ̇) + f v ϕ̇.
(3.5)
which can describe many of the friction characteristics with speed. This model
structure is commonly used and was described in Bo and Pavelescu (1982). For
the fixed α = 1, the model simplifies to the Tustin model, introduced by Tustin
(1947). Notice that different choices of the g− and h are possible in the LuGre
model.
Figure 3.3 shows the response of the LuGre model and the corresponding static
model with g− and h chosen according to (3.4) and (3.3). The simulation was performed with ϕ̇ as half a period of a triangular waveform with different slopes A.
In the acceleration phase, the transition from bl to ehl gives less friction torques
than during deceleration. The higher A, the more pronounced are the dynamic
effects.
i f is commonly called static friction parameter. An alternative nomenclature was adopted to
s
make a distinction between the dynamic/static friction description.
3.4 Summary and Connections
29
3.4 Summary and Connections
This chapter presented a brief overview of friction and wear from both empirical and phenomenological perspectives. The summary of the effects of different
factors to the friction curves in Figure 3.2 gives a good idea behind the motivation and challenges of this work. The effects of temperature, load and wear in
the figures are in comparable orders of magnitude. Attempting to determine the
wear status based on observed changes to friction is therefore challenging. Load
and temperature changes will always be present in applications and the diagnosis
solutions must be able to cope with them.
The models presented in this chapter are only dependent on speed (and z). Extended joint friction models are proposed in Papers A and B to describe the effects of load, temperature and wear to friction. More realistic friction models
are important for control, simulation and diagnosis of industrial robots. These
models are used during the design and verification of the wear diagnosis methods proposed in Papers B and C. Simulation studies are a cost and time efficient
alternative to wear experiments and have allowed for a more detailed analysis
and evaluation of the diagnosis solutions in Paper D.
4
Basics of Fault Diagnosis
Fault diagnosis concerns the detection of an abnormal behavior and determination of its cause based on domain knowledge, premises and observations. Fault
diagnosis is a multidisciplinary topic and of relevance in many different fields.
The related literature is vast and the terminology and approaches can considerably vary across the different communities. With origins closer to control theory
and statistical decision making, the field is known as Fault Detection and Isolation (fdi) (Isermann, 2006; Ding, 2013). In artificial intelligence, diagnosis is
studied by the dx (Diagnosis) community (Reiter, 1987; De Kleer and Williams,
1987; Cordier et al., 2004). The area recognized as condition monitoring provides important tools for diagnosis and has origins in Maintenance Engineering
(Rao, 1998). In Machine Learning, fault diagnosis can relate to classification problems (Kotsiantis, 2007).
It is outside the scope of this thesis to provide an extensive discussion of the
different approaches and terminology used in fault diagnosis. Instead, the presentation aims at familiarizing the reader with the problems and contextualizing
the methods developed in this work. The presentation and terminology used
are closest to the ones found in the Fault Detection and Isolation literature and
is mainly based in Gustafsson (2000); Isermann (2006); Basseville and Nikiforov
(1993); Ljung (1999).
4.1 Overview of Fault Diagnosis
The fault diagnosis process can be divided in two main functions as depicted
in Figure 4.1. In fault detection, data collected from the monitored system are
processed and compared to available knowledge about the system to determine
31
4 Basics of Fault Diagnosis
32
Knowledge
Faults
Disturbances
Monitored
System
Data
Fault
Detection
Symptoms
Fault
Isolation
Diagnosis
Fault Diagnosis
Figure 4.1: Overview of the fault diagnosis process. Data collected from the
system are processed by the fault detection methods to generate symptoms
that indicate the presence of abnormalities. The symptoms are analyzed in
fault isolation to produce a diagnosis consistent to the knowledge embedded
in the diagnosis solution and observations.
presence of abnormalities. Symptoms are the outputs of fault detection and are
manifestations of one or more faults, indicating presence of abnormalities. Diagnosis of complex systems typically makes use of several fault detection methods,
each based on partial information of the system. Each possible fault manifests
itself in subsets of all the possible symptoms. In fault isolation, this information
is used to find the faults that are consistent to all observed symptoms, i.e., it generates a diagnosis. Some fault diagnosis schemes also include fault identification
where the size and the time profile of the faults are determined.
This work is mainly concerned with the design and evaluation of fault detection
methods. This is motivated from the robotics application where the objective is
to generate symptoms that can relate to an abnormal friction behavior caused
by excessive wear levels. A determination of which component in the joint that
is faulty, e.g., whether a bearing or a shaft, is of less importance. It is important though that a faulty state is detected in an early stage, so that appropriate
maintenance actions can be scheduled before a failure. The discussion for the
remaining part of this chapter is focused on the design of fault detection methods. For further information on fault isolation, the reader is referred to Isermann
(2006); De Kleer and Williams (1987).
4.1.1 Fault detection
An overview of fault detection can be seen in Figure 4.2. The monitored system is
affected by faults and disturbances and generates data. The data are processed to
extract characteristic features (properties) of the system (e.g., parameters, residuals, signal spectra). The behavior of the extracted features is compared to their
behavior found under nominal conditions, generating one or more test quantities.
The test quantities measure how far the observed features are from the nominal
case. Test quantities are input to a decision rule (e.g., a threshold check or a statistical test) to determine presence of an abnormality, i.e., it generates symptoms.
The combined tasks of feature extraction and behavior comparison are performed
by the fault detection algorithm (fda). Depending on the strategy to fault detec-
4.1 Overview of Fault Diagnosis
33
Knowledge
Faults
Disturbances
Monitored
System
Data
Feature
Extraction
Features
Behavior
Comparison
Test
Quantities
Decision
Rule
Symptoms
Fault Detection Algorithm
Figure 4.2: Overview of a fault detection scheme. The monitored system is
affected by inputs, e.g., faults and disturbances, and generates data. Features are extracted from the data which are compared against their nominal
behaviors, generating test quantities. The test quantity measures conformity
between the observed and nominal behavior of the features which is tested
for by the decision rule, generating a symptom.
tion, there is little distinction between the different tasks in fault detection, e.g., a
classifier makes a direct map from data to a symptom or to a diagnosis and thus
involves feature extraction, behavior comparison and a decision rule. The division in subtasks is made to fit the proposed methods into a common framework.
When fault detection is performed with no perturbation of the system’s functions,
during operation, it is called an on-line solution, otherwise it is denoted off-line,
as "off-the-line", and will reduce the system’s availability. When fault detection is
performed by actively exciting the system, it is called active. If it is performed
by passively studying the system, it is called passive. When fault detection is
performed at each new observation (e.g., at each incoming data samples) it is
a sequential solution, otherwise it is a batch solution. Sequential solutions with
finite memory and computational requirements are also denoted as recursive and
are important for implementation issues.
Example 4.1: An off-line passive method for wear monitoring
The wear processes inside a robot joint cause an eventual increase of wear debris
in the lubricant. The iron content of lubricant samples taken from the robot joint
can thus be used to indicate the condition of the joint. The study of wear debris
is known as ferrography and was first introduced by Seifert and Westcott (1972).
Since then, the science has evolved and helped to understand wear related phenomena, see Roylance (2005) for a historical review. In Figure 4.3, different types
of wear particles are shown. In most applications, the collection of lubricant samples can only be performed when the system is turned off, in an off-line manner,
followed by laboratory analysis. Notice that no dedicated excitation of the system
is needed, so this is also a passive method.
The design of fault detection methods makes extensive use of knowledge about
the monitored system. This knowledge might come from models, assumptions,
data, an operator, an expert, etc. The next section presents an important type
of knowledge representation, models of systems and faults. In general, the extent to which a system model is known can considerably affect the design and
4 Basics of Fault Diagnosis
34
(a) Spherical.
(b) Laminar.
(c) Cutting.
Figure 4.3: Images of different types of wear particles from ferrography.
The mechanical condition of the system may be determined from analysis
of the characteristics of the wear particles, e.g., the type, shape, frequency,
etc. (Pictures extracted from Machalíková et al. (2010)).
performance of diagnosis solutions.
4.1.2 Models of systems and faults
In order to choose the diagnosis solution, it is important to understand the behavior of the system and its dependencies on the faults. This can be achieved
with the use of models. A system model describes the relationships between the
variables affecting the system. The measured output data is denoted by y which
is affected by deterministic, z, and random, v, inputs. The relationship between
the variables is described by a map
y = h(z, v).
(4.1)
The random inputs v are unknown (e.g., noise), while z could have both known
and unknown components. The known components of z include control inputs
u and reference signals r. Unknown components of z include disturbances d
and faults f . The known signals y, u and r are the data input to the diagnosis
process. For the design and evaluation of diagnosis methods, it is important to
understand how the unknown components v, d and f affect the available data so
that the different effects can be identified correctly.
When the system model map is a function of some parameters θ, this map is
called a model structure, M,
M:
y = h(z, v; θ).
(4.2)
A particular choice of parameters, θ′ , leads to a model instance, M(θ′ ), of the
model structure M. Modeling can be performed based on first principles, e.g.,
from the laws of physics, where the parameters of the resulting model will have
some physical meaning. In case there are parameters with unknown values, these
can be determined empirically, e.g., from an identification procedure. An alternative to modeling from first principles is to choose the model based on how well it
4.1 Overview of Fault Diagnosis
35
describes the data, where the parameters have no obvious physical interpretation.
Fault Models. Of special importance is the modeling of faults. The fault model
chosen must reflect the physical effects of the fault. Faults can be categorized by
their time behavior and by the manner they affect the system (Isermann, 2006).
With respect to the time behavior, fault models are often categorized as:
Abrupt, affect the system abruptly, stepwise.
Incipient, develop gradually with time.
Intermittent, affect the system with interruptions.
According to how they affect the system, fault models are categorized as:
Additive, effectively added to the signals describing the model.
Multiplicative, acting on a parameter of the system. For example, changing a
parameter θ of the model structure M.
Structural, introduces new governing terms to the describing equations of the
system. For example, changing the model structure M.
The choice of fault model can be motivated from the physical process in which
faults take part. For example, a bias like error in a sensor can be modeled as an
additive fault, while an incorrect specification of a payload mass for a robot can
by modeled as a multiplicative fault since it affects a parameter for the model
describing its dynamics.
Example 4.2: An industrial robot under wear and temperature effects
With references to Section 2.2 and Chapter 3, a manipulator can be described in
a simplified manner by a multi-body rigid model
M(ϕ)ϕ̈ + C(ϕ, ϕ̇)ϕ̇ + τ g (ϕ) + τ f (ϕ̇, τ ℓ , ξ, ̟) = τ
(4.3)
where the parametric dependencies are not shown for simplicity. The friction
torques τ f ( · ) are described as a function of angular speed ϕ̇, manipulation load
torque, τ ℓ , temperature, ξ, and wear, ̟. The fault f relates to the wear levels
̟. Wear changes the behavior of friction in a gradual manner and can be modeled as a change in the friction model parameters, in which case it is an incipient
multiplicative fault. The measured (known) outputs y are the angular positions
ϕ, from which is also possible to achieve angular speeds ϕ̇. The control inputs u
are the applied torques i τ which depend on a reference signal, r, not described
in (4.3). The measured quantities are corrupted by noise, v, not described in
(4.3). The loads, τ ℓ , and temperatures, ξ, are unknown and considered as disturbances d.
i Based on the simplification that the relation between current and applied torque is given by a
constant. See Section 2.4 for details.
4 Basics of Fault Diagnosis
36
4.2 Fault Detection Algorithms
An important tool to support the design of fault detection algorithms is a system model structure M. Two approaches based on a system model are briefly
discussed.
Residuals. A nominal model instance, M(θ0 ), is used to reconstruct the output
from the data, creating an analytical redundancy b
y(k, θ0 ) of the system outputs y k at each sample time k. The difference
ε(k, θ 0 ) = y k − b
y(k, θ 0 ),
also known as the model residuals measures deviations between the model
and observations and can be used directly as test quantities. The modelbased design of residual generation methods, also known as fault detection
filters, has received much attention in the literature, see e.g., Frank and Ding
(1997); Liu and Zhou (2008); Ding (2013).
Parameter estimation. The unknown parameters for the model instance, M(θ),
are chosen such that the resulting model best explains the data in some
way. For instance, the least squares criterion chooses θ such that the sum
of squared errors is minimized, i.e.,
X
2
b
θ = arg min
yk − b
y(k, θ) .
θ k
Test quantities can be defined based on a comparison between b
θ and a nominal region for the parameters Θ0 .
Given an estimate b
θ, the residuals ε(b
θ) = y − b
y(b
θ) can also be used as test
quantities which are suitable for diagnosis of structural faults.
Remark 4.1. Parameter estimation techniques are a natural choice for multiplicative and
structural fault models, while the typical formulation for residual generation considers
additive fault models. Nevertheless, these methods can be used interchangeably, Isermann
(2006).
When a system model structure is not available, alternative solutions are possible.
These solutions will typically require expert knowledge about the data or extra
(redundant) sensor information. An example of such expert knowledge is found
in the analysis of features of measured signals, e.g., their frequency responses.
Some fault detection algorithms that relate to the research results presented in
this thesis are described in more details next.
4.2.1 Parameter estimation
The objective is to identify the unknown parameters of a known model structure
from the data. Algorithmically, the solutions will depend on the model structure
and whether a recursive method is sought. To illustrate this class of methods, the
maximum likelihood approach is described next. The presentation is based on
4.2 Fault Detection Algorithms
37
Ljung (1999).
Let the mechanism generating the data y k be described by its probability density
p(y k | θ) which
h is a function of unknown
i parameters θ. For a sequence of observations YN = y 1 , · · · , y k , · · · , y N , the joint density is denoted p N (YN | θ).
Given a particular observation of the data YN∗ , the likelihood function is defined
as the joint data density evaluated at YN∗ as a function of the parameters θ, i.e.,
L(θ) , p N (YN∗ | θ). The likelihood function L(θ) relates to the probability of
an observation YN∗ for a certain choice of parameter θ. The maximum likelihood
estimate is defined as the parameter value maximizing the likelihood function
b
θ = arg max L(θ) = arg max log L(θ)
θ
θ
(4.4)
where the last equivalence follows since maximizing a function is equivalent to
maximizing its logarithm (a monotonic transformation).
To justify the use of the maximum likelihood estimate, consider first the following criterion for assessing the performance of an estimator. Let the true parameter be denoted by θ0 , the quality of an estimate b
θ can be assessed by the mean
square error matrix, defined as
T θ − θ0
= Σ b + b(b
θ)b(b
θ)T ,
(4.5)
P=E b
θ − θ0 b
θ
where Σ b is the covariance matrix and b(b
θ) is the bias defined respectively as
θ
h i h iT h i
Σb , E b
θ−E b
θ b
θ−E b
θ
, b(b
θ) , E b
θ − θ0 .
(4.6)
θ
For any unbiased estimator, i.e., b(b
θ) = 0, the following inequality, known as the
Cramér-Rao lower bound, applies,


!
!  ∂ log L(θ) ∂ log L(θ) T 
0 −1
0
 ,
(4.7)
P ≥ F (θ ) ,
F (θ ) , E 

∂θ
∂θ
0
θ =θ
where F (θ0 ) is known as the Fisher information matrix. Note here that a sensible
approach to experiment design is, if possible, to affect the likelihood function
such that the inverse Fisher information matrix is made small in some sense, thus
improving the achievable performance for any unbiased estimator.
Suppose now that each data y k are independently and identically distributed
Q
so that the joint density p N (YN | θ) = N
k=1 p(y k | θ). Then, as the number of
data tends to infinity, N → ∞, the maximum likelihood estimate given by (4.4)
converges in distribution to a Gaussian centered around θ0 with covariance given
by the inverse of the Fisher information matrix,
−1 √ .
(4.8)
θ − θ0 ∼ As N 0, F θ0
N b
That is, the maximum likelihood estimate is asymptotically unbiased and has
the smallest possible covariance, achieving the Cramér-Rao lower bound. The
4 Basics of Fault Diagnosis
38
maximum likelihood estimate is illustrated in Example 4.3.
Example 4.3: Scalar linear regression under additive Gaussian noise
Let the model for each datum yk be described by a linear regression with additive
uncertainty vk ,
yk = φ(k)T θ0 + vk ,
(4.9)
where φ(k) : Rd 7→ R is a deterministic function and θ0 ∈ Rd are the true parameters. For N observations, y ∈ RN , the model can be written in vector form
as
h
iT
y = Φθ0 + v, Φ , φ(1), . . . , φ(N ) .
(4.10)
Considering v as a zero mean Gaussian uncertainty with covariance Q −1 , i.e.,
v ∼ N (0, Q−1 ), the joint density is then p N (y | θ0 ) = N (y; Φθ0 , Q −1 ), where the
Gaussian distribution is defined as
N (x; µ, Σ) , |2πΣ|−1/2 e − 2 (x −µ)
1
T
Σ−1 (x −µ)
.
(4.11)
For a given observation y ∗ , the maximum likelihood estimate of the parameters
reduces to a weighted linear least squares and is given by
b
θ = arg max log N (y ∗ ; Φ θ, Q −1 )
θ
−1
1
ΦT Qy ∗ .
= arg min (y ∗ − Φ θ)T Q (y ∗ − Φ θ) = ΦT QΦ
θ 2
(4.12a)
(4.12b)
Because b
θ is a linear function of the data y ∗ , it is also a Gaussian distributed
random variable and is given by
−1
b
θ ∼ N (θ0 , Σ b ),
Σ b , ΦT QΦ ,
(4.13)
θ
θ
the estimate is thus unbiased and it is possible to show that Σ b achieves the
θ
Cramér-Rao lower bound even for finite N .
Behavior comparison. The result in (4.8) suggests the generation of test quantities in two different manners. Either the d estimated parameters b
θ ∈ Rd are
0
compared to nominal values θ one by one, or they are compared jointly. In the
first case, the following test quantity can be used for each of the ith parameter
q=
[b
θ]i − [θ0 ]i
∼ As N (0, 1),
q
[Σ b ]ii
θ
1 < i < d.
In the second case, the following test quantity can be used
T
b
q= b
θ − θ0 Σ−1
θ − θ0 ∼ As Xd2 ,
b
θ
(4.14)
(4.15)
where Xd2 is the chi-square distribution with d degrees of freedom. Because the
(asymptotic) distribution for the test quantities are known under the nominal
4.2 Fault Detection Algorithms
39
0.15
10 20 30 40 50 60 70
τf
0.1
0.05
0
50 100 150 200 250
ϕ̇ [rad/s]
(a) Friction curves.
q
q
20
10
0
−10
fc
fs
fv
0
20
40
k
60
(b) Test quantities as in (4.14).
500
400
300
200
100
0
0
20
40
k
60
(c) Test quantity as in (4.15).
Figure 4.4: Test quantities for the detection of changes in the parameters of
a friction model. The friction data are shown in (a) with colormap relating to
the experimentation index k . For each friction curve, i.e., for each k , the parameters of a friction model are estimated and the test quantities defined in
(4.14) and (4.15) are computed and shown in figures (b) and (c) respectively.
case, a decision rule is readily available from confidence intervals of the related
distributions. In some cases, such as in Example 4.3, the confidence intervals are
exact for finite N . A test based on (4.15) is used for example in Peretzki et al.
(2011) to determine whether estimated parameters significantly deviate from
zero, i.e., θ0 = 0, and the test quantity is used as a measure of the data quality for identification. Example 4.4 illustrates the use of parameter estimates for
detection of changes in the parameters of a friction model.
Example 4.4: Friction change detection for a robot joint under wear effects
The Tustin friction model presented in Section 3.3 is a common choice to describe
the behavior of friction. For a fixed Stribeck velocity, ϕ̇s , and for positive speed
values, the Tustin model can be written as a linear regression
h
iT h
i
ϕ̇
ϕ̇
−
f c , f s , f v = φ(ϕ̇)T θ. (4.16)
τ f (ϕ̇, θ) = f c + f s e ϕ̇s + f v ϕ̇ = 1, e − ϕ̇s , ϕ̇
A dedicated experiment is used to collect friction data, τ f ∈ RN , from a robot
joint. From this experiment N = 13 friction data points are retrieved. It is assumed that these data can be described by (4.16) under an additive zero mean
Gaussian uncertainty with covariance Q −1 = γ I and where the Stribeck speed is
fixed to ϕ̇s = 12.85. The resulting data model is of the form given in Example 4.3.
The data collection experiment is repeated K = 72 times during accelerated wear
tests and the resulting friction curves are shown in Figure 4.4a. At time k, max-
4 Basics of Fault Diagnosis
40
imum likelihood estimates of the parameters are found as given by (4.12b). A
nominal parameter value θ0 is assigned from healthy data and the test quantities based on (4.14) and (4.15) are computed using an estimate of γ found from
the data. The resulting test quantities are shown in Figures 4.4b and 4.4c together with 99% confidence intervals. For these data, it is known that no significant effects of wear are present before k = 60. However, as can be seen, the test
quantities leave the confidence intervals around k = 30 and k = 50. A possible
explanation for this behavior is that another variable other than speed, such as
temperature, is causing the variations to friction and a more reliable alternative
is needed to avoid false alarms.
4.2.2 Signal-driven methods
In many applications, the available data are signals. It is possible to extract
information about the system condition by only considering characteristics of
these signals. In order to reveal relevant features of the signals for fault detection, transforms are widely used in signal-driven methods. A transform is used
to “map” a signal from its original domain to an alternative domain. Features of
the data in the alternative domain may reveal more information about the faults.
An integral transform is any transform T of the form
y(ν) = T{y(t)} =
Zt1
κ(t, ν)y(t) dt.
(4.17)
t0
where y(t) is the original signal, y(ν) = T{y(t)} is the transformed signal, function
of ν, and κ(t, ν) is a kernel function. Several types of integral transforms and
discrete transforms can be defined, e.g., Fourier transform, Wavelet transform,
Karhunen-Loève transform, Radon transform, etc. Each transform will highlight
different properties of the data in the transformed domain.
For example, the Fourier transform F{y(t)} is a transform with
κ(t, ν) = e −itν ,
t0 = −∞,
t1 = ∞.
(4.18)
When t is time, ν is frequency. The transformed signal y(ν) = F{y(t)}, is said to
be the frequency representation of y(t). The analysis of data in the frequencydomain has found particular success in the monitoring of rotating machines,
Taylor (1994); de Silva (2007). Example 4.5 illustrates the use of frequency domain analysis for monitoring of backlash in gearboxes.
Example 4.5: Backlash monitoring in the frequency domain
This example is based on Sander-Tavallaey and Saarinen (2009) where backlash
is studied in drives equipped with compact gearboxes. An increase of backlash
will introduce additional resonance peaks to the frequency content of the drive
response. Spectral analysis can therefore be used to indicate backlash changes.
A dedicated test-cycle, displayed in Figure 4.5a, is used to excite the drive unit. In
41
200
20
100
0
−20
−40
0
0
ϕ̇
τ
−100
5
t [s]
10
−200
15
(a) Test-cycle excitation.
3.5
3
2.5
2
1.5
1
0.5
0
Backlash
Healthy
|F{τ(t)}|
40
ϕ̇ [rad/s]
τ [Nm]
4.2 Fault Detection Algorithms
0
10
20 30
ν [Hz]
40
50
(b) Torque spectra.
Figure 4.5: Backlash monitoring through spectrum analysis. The drive is
excited with a test-cycle in an off-line manner as displayed in (a). The frequency spectrum of the torque signals are shown in (b). Notice the increased
resonance peak around 47 Hz for the unit with increased backlash compared
to the healthy unit.
Figure 4.5b, spectra estimates for the torque signals are shown for a healthy unit
and for a unit with increased backlash levels. As can be seen, there is an increase
of the frequency response around 47 Hz. In the paper, this deviation is used
to generate a test quantity used for backlash monitoring. The proposed method
takes only a few seconds to execute and does not consider additional vibration
measurements, which are common for this type of method. Notice that this is an
off-line active solution since it is based on a test-cycle.
Behavior comparison. As in Example 4.5, specific characteristics of the spectrum can be used as test quantities, allowing for an automated fault detection.
With knowledge of the behavior of the entire spectrum under a certain condition,
test quantities can also be defined by a direct comparison between spectra. For
example, let y0 (ν) be a spectrum representing a known behavior, e.g., fault-free,
and y(ν) be the spectrum found from incoming test data y, the log-spectral distance between them is
y0 (ν) (4.19)
q = log
,
y(ν) δ
and can be used as a test quantity to determine conformity to the reference spectrum. The choice of norm will highlight different characteristics, e.g. δ = 2 leads
to the mean quadratic distance and δ = ∞ leads to the maximum deviation. See
Basseville (1989) for more on spectral distances.
4.2.3 Data-driven methods
Statistical features extracted from the data are also valuable for fault diagnosis.
Methods based on principal component analysis, partial least squares, linear discriminant analysis and classification methods have found success in many applications, see, e.g., Yin et al. (2012) for an overview of techniques.
4 Basics of Fault Diagnosis
42
An important feature for data-driven methods is the distribution of the data p(y),
from which different test quantities can be defined. A density estimator relevant
to this work is described next. The discussion follows with the presentation of
methods defined in the distribution domain and a description of linear discriminant analysis.
Kernel density estimator
A nonparametric estimate of the distribution p(y) based on the data vector y
can be achieved from the empirical characteristic function. For a scalar random variable with probability density function p(y), the characteristic function
ϑ(ν) : R → C is defined as (Durrett, 2010):
ϑ(ν) = E [e
νy
]=
Z∞
−∞
e νy p(y) dy = F−1 {p(y)}2π,
where F−1 { · } is the inverse Fourier transform. So the density function can be
found from the characteristic function through its Fourier transform. Following
Parzen (1962), given the sample y ∈ RN , the empirical estimate of ϑ(ν) is
N
1 X νyn
b
e
ϑ(ν) =
,
N
n=1
b
and the objective is to retrieve the density function from ϑ(ν).
This is essentially
a spectrum estimation problem. A direct estimation of the density function from
b
the Fourier transform of ϑ(ν)
will however lead to an estimate with variance
b is
that does not decrease with N (Ljung, 1999, Section 6.4). To avoid this, ϑ(ν)
multiplied with a weighting function ψh (ν) = ψ(hν). The weighting function is
typically symmetric, satisfying ψ(0) = 1 and tends to zero when ν tends to infinity.
The density estimate is then given by
o
1 nb
1
b
p (y) =
F ϑ(ν)ψ(hν) =
2π
2π
=
=
1
2π
Z∞
−∞
b
e −νy ϑ(ν)ψ(hν)
dν
Z∞
N
N
yn −y
1 X ν(yn −y)
1 X 1
e
ψ(hν) dν =
e ν ( h ) ψ(hν) d(hν)
N
Nh
2π
−∞
N
X
1
Nh
Z∞
n=1
n=1
n=1
N
y −y
1 X
κ n
=
κh (y − yn ),
h
N
−∞
(4.20)
n=1
where κh (y)h = F−1 {ψh (ν)}. The resulting estimate is known as a kernel density estimate (kde) and can also be generalized to the multidimensional case (Cacoullos,
1966). The function κh (y) is a kernel function, satisfying κh ( · ) ≥ 0 and that integrates to 1. Typical kernel functions and their Fourier transforms are shown in
Figure 4.6. The bandwidth parameter h controls the smoothness of the resulting
estimate, increasing the smoothness for larger values of h. When h → 0, the ker-
4.2 Fault Detection Algorithms
1.2 h = 1
1 h=2
0.8 h = 3
0.6
0.4
0.2
0
−0.2
−20−10 0 10 20
ν
1 h=1
=2
0.8 h
h=3
0.6
0.4
0.2
0
−5
0
y
1.2 h = 1
1 h=2
0.8 h = 3
0.6
0.4
0.2
0
−0.2
−20−10 0 10 20
ν
1.2 h = 1
1 h=2
h=3
0.8
0.6
0.4
0.2
0
−10 −5 0
ν
h=1
h=2
h=3
0.3
κh (y)
0.2
0.1
5
0
−5
(b) Triangular.
0
y
5
(c) Gaussian.
1.2 h = 1
1 h=2
h=3
0.8
0.6
0.4
0.2
0
−5
0
5 10
ν
ψh (ν)
ψh (ν)
(a) Uniform.
ψh (ν)
0.4
κh (y)
ψh (ν)
replacemen
43
5
Figure 4.6: Kernel functions (upper row) and their respective Fourier transforms (bottom).
nel function approaches a Dirac delta and the resulting estimate will be a set of
impulses located at the data points. For a detailed treatment of kernel density estimators and criteria/methods for choosing h see Parzen (1962); Cacoullos (1966);
Bowman and Azzalini (1997); Jones and Henderson (2009).
Distribution domain methods
Test quantities can be generated based on distribution domain features. This is
a valid approach since the effects of faults often appear as changes in the data
amplitude. Similar to spectrum analysis, test quantities can be generated from
specific characteristics of the data distribution or from the entire distribution.
Some approaches are discussed next.
Distribution peak. Rzeszucinski et al. (2012) propose the use of the peak of the
Gaussian density as a test quantity for diagnosis of gearboxes based on vibration
data. The test quantity is given by q = 1 − max p(y), where p(y) is the (univariate)
y
Gaussian density function. The Gaussian density has mode (maximum) at the
mean µ and the test quantity simplifies to
q = 1 − max p(y) = 1 − p(µ) = 1 − √
y
1
2πσ
,
(4.21)
and depends only on an estimate of the standard deviation, σ, which is a measure
of the data spread.
4 Basics of Fault Diagnosis
44
0.01
0
0
2
4
6 8
t [sec]
30
25
20
15
10
5
10
q̄(k)
(a) Torque data sequences.
500
400
300
200
100
0
8 35
30
6 25
4 20
15
2 10
5
0−0.5
b
p (y)
τ
0.02
0
τ
0.5
1
(b) Density estimates.
A
B
C
0
5 10 15 20 25 30 35
k
(c) Normalized test quantities.
Figure 4.7: Monitoring of a wear fault in an industrial robot joint in the distribution domain. The torque sequences are shown in (a) and their respective
kernel density estimates are shown in (b); the colormaps relate to the experiment length k . The normalized response for the test quantities considered
are shown in (c).
Data likelihood. Estimates of the data distribution can also be used to generate
test quantities. With availability of a nominal data density estimate b
p 0 (y), test
quantities can be generated based on the likelihood that incoming data y present
under this distribution. For batches of data y ∈ RN , a test quantity can be defined
for instance as the (average negative log) likelihood under b
p 0 (y), i.e.
q=−
N
1 X
log b
p 0 (yn ).
N n
(4.22)
Large values of this test quantity would indicate a change in behavior. This idea
has been suggested by Agarwal (2007); Desforges et al. (2000); Yeung and Chow
(2002) where flexible density models are used.
Comparison of distributions. An alternative is to compare a nominal density
model b
p 0 (y) against a density estimate b
p (y) achieved from test data. The comparison can be made with the use of distances between distributions, such as the
symmetric Kullback-Leibler divergence, or Kullback-Leibler distance, given by
q = KL b
p 0, b
p , DKL b
p 0 ||b
p + DKL b
p ||b
p0 ,
(4.23)
4.2 Fault Detection Algorithms
45
where DKL ( · || · ) is the Kullback-Leibler divergence
∞
Z
b
p 0 (y)
0
b
b
dy.
DKL p ||b
p ,
p 0 (y) log
b
p (y)
−∞
The test quantity in (4.23) is suggested in Paper C for fault diagnosis. Other examples of divergences and distances are possible, see Reid and Williamson (2011);
Basseville (1989) for more. Example (4.6) illustrates the use of different distribution domain test quantities for monitoring of wear in a robot joint.
Example 4.6: Distribution domain methods for wear monitoring in a robot
Torque data collected from the execution of a test-cycle for a robot undergoing
accelerated wear tests are considered. The data are shown in Figure 4.7a and
contain K = 36 batches of torque sequences, each containing N = 7440 samples.
For these data, it is known that no significant wear is present until batch k = 25.
Kernel density estimates are found for each data batch with a Gaussian kernel
function and are displayed in Figure 4.7b. An inspection of the Figures 4.7a and
4.7b reveals that the effects of the fault are more easily distinguishable in the
distribution domain.
The test quantities described in Equations (4.21), (4.22) and (4.23) are computed
for each batch k and are denoted A, B and C respectively. For test quantities B
and C, the density estimates used are those shown in Figure 4.7b. The nominal
density estimate b
p 0 (τ) is assigned for the first batch k = 1. For a comparison,
each test quantity qk is normalized according to qk = (qk − µ0 )/σ 0 where µ0 and
σ 0 are the mean and standard deviation of the test quantity computed for k ≤ 5.
The results are shown in Figure 4.7c, where can be seen that all test quantities
respond to the wear changes while test quantity C gives the clearest response.
Linear discriminant analysis
Given a data vector y ∈ RN , a discriminant function reduces the data dimension
to L < N according to a linear transformation
h
i
y = W T y, W = w1 , · · · , w L
where the weight matrix W ∈ RN ×L is chosen based on a criterion to support
the discrimination of different behaviors of the data, e.g., faulty or healthy. The
resulting weighted data vector y can be used as test quantities for fault detection.
The weight matrix W is chosen based on labeled data, i.e., data sets where the
condition of the system is known. As an illustration, consider availability of data
sets corresponding to the classes of normal and faulty operations
h
i
f
f
f
YK00 = y 01 , · · · , y 0K0 , YKf = y 1 , · · · , y Kf
respectively. Take the case L = 1 and W = w, the mean of YK00 (and similarly for
4 Basics of Fault Diagnosis
46
f
YKf ) in the transformed domain is
K0
K0
1 X
1 X
0
w T y 0k = w T
µ =
yk =
K0
K0
k
µ0
k


K0
 1 X


y 0k  = w T µ0 ,

K0
k
where
is the sample mean of the original data. The sample variance for ȳ is
given by
K0 K0 K0 2
2
2 2
1 X
1 X
1 X
w T y 0k − µ0 =
w T y 0k − µ0
yk − µ0 =
K0
K0
K0
k
k
k


K
K
0
0
 1 X
1 X T 0 0T

T
T
T
T
T 
0
0
0
0

w (y k y k )w − w (µµ )w = w 
y k y k − µ µ  w
=
K0
K0
k
k


K0
 1 X

= w T 
(y 0k − µ0 )(y 0k − µ0 )T  w = w T Σ0 w.
K0
σ20 =
k
where Σ0
is the sample covariance matrix. The Fisher linear discriminant chooses
w that maximizes the quotient between average class separation and total variation in the transformed domain, this criterion is written as
2
w T (µf − µ0 )
(µf − µ0 )2
w T (µf − µ0 )(µf − µ0 )T w
=
=
,
(4.24)
V (w) = 2
w T (Σ0 + Σf )w
w T (Σ0 + Σf )w
σ0 + σ2f
which is a generalized Rayleigh quotient. Defining e
µ , µf − µ0 and Σ , Σ0 + Σf ,
for a positive definite Σ, the maximum of V (w) follows from the Cauchy-Schwarz
inequality
2
(Σ1/2 w)T (Σ−1/2e
µ)
(w T Σw)(e
(w T e
µ )2
µT Σ−1e
µ)
=
≤
=e
µT Σ−1e
µ,
V (w) = T
w Σw
w T Σw
(w T Σw)
and the bound is attained for the optimum
−1 w∗ = Σ−1e
µ = Σ0 + Σ f
µ f − µ0 .
In case Σ is positive semi-definite, a regularization term can be added, see, e.g.,
Friedman (1989).
4.3 Decision Rule
Test quantities can be tested jointly or separately to generate symptoms. The
choice will depend on the objectives of fault isolation and on how the generated
symptoms can relate to faults. For the presentation here, it is considered that
each test quantity is tested separately, so that as many symptoms as possible are
generated. The presentation is mainly based on the literature of detection theory,
see, e.g., Basseville and Nikiforov (1993); Van Trees (2001).
4.3 Decision Rule
47
A test quantity, q, is used to test whether the behavior of the extracted features
conform to a nominal behavior or not. Each of these conjectures can be seen as
a hypothesis. The null hypothesis H0 corresponds to the case where the features
and their nominal behavior agree and the alternative hypothesis is H1 . Due to the
effects of noise and random disturbances, the test quantity will present a random
behavior that will differ according to the hypothesis present. This can be modeled
by the statistical behavior of the test quantity under the hypotheses,
H0 : q ∼ p 0 (q),
H1 : q ∼ p 1 (q).
(4.25)
A decision rule takes the test quantity as input and chooses either of the hypotheses. A general model for the decision rule is thus φ(q) : R 7→ {0, 1} where φ(q) = 0
implies that H0 is accepted, otherwise H1 is chosen. Because only two outcomes
are possible, the decision rule can be specified by considering the acceptance region, R0 , where φ(q) = 0,
R0 = {q : φ(q) = 0}
(4.26)
and its complement R0c is the set giving the alternative outcome φ(q) = 1.
Performance of the decision rule can be measured by the probabilities of making
erroneous decisions, given by
Z
h
i Z
Pf = Pr φ(q) = 1 | H0 is true = p 0 (q) dq = 1 − p 0 (q) dq,
(4.27a)
R0c
i Z
Pm = Pr φ(q) = 0 | H1 is true = p 1 (q) dq
h
R0
(4.27b)
R0
where Pf is the probability of false alarm, i.e., deciding incorrectly for H1 , and
Pm is the probability of missed detection, i.e., deciding incorrectly for H0 . The
complement 1 − Pm is also denoted the probability of (correct) detection, Pd . In
defining the decision function, φ( · ), different objectives are possible but in general low Pf and Pm are sought.
The Neyman-Pearson criteria attempt to minimize one of the error probabilities
while the other is constrained to an upper bound, i.e.
min Pm
R0
s.t. Pf ≤ Pf′ ,
or, alternatively,
min Pf
R0
s.t. Pm ≤ Pm′ ,
(4.28)
where Pf′ and Pm′ are pre-specified values. In general, the decision errors cannot
be made arbitrarily small and a compromise must be made. The next section
introduces a simple decision rule.
4 Basics of Fault Diagnosis
48
4.3.1 Thresholding
The simplest and most common decision rule is to consider direct thresholding
of the test quantity, in which case the acceptance region is given by
R0 = {q | q ≤ ~} .
For a threshold check, the error probabilities can be computed as
Pf =
Z∞
~
0
p (q) dq,
Pm =
Z~
p 1 (q) dq,
−∞
and the choice of threshold ~ can be motivated from a Neyman-Pearson criterion or chosen based on available knowledge of the behavior of the test quantity.
Example 4.7 illustrates the use of thresholding for a test quantity used to detect wear changes in a robot joint and illustrates some of the compromises in the
threshold selection.
Example 4.7: Detection of wear changes in a robot joint
The test quantity defined in (4.23) is considered for torque data collected from
a robot joint under accelerated wear tests as in Example 4.6. Given two batches
of torque data τ m and τ n , the test quantity is q = KL (b
p m, b
p n ) where b
p m (τ) and
n
b
p (τ) are kernel density estimates from the respective torque batches.
As presented in Example 4.2, the available torque data are affect by the wear level
in the joint, ̟, as well as the temperature in the joint, ξ, i.e., τ(̟, ξ). Temperature
is considered as a disturbance since it is not measured. Based on simulations, the
torque sequences are generated under the two setups (hypotheses)
H0 :
H1 :
τ m (̟ = 0, T ) and τ n (̟ = 0, T ),
τ m (̟ = 0, T ) and τ n (̟ = ̟c , T ),
i.e., no wear effects are present for H0 and a critical wear increase of size ̟c = 35
is present for H1 . For analysis of the effects of temperature disturbances, ξ is
considered random with a uniform distribution
ξ = 30◦ C
ξ ∼ U (ξ, ξ + ∆ξ ),
and the value of ∆ξ relates to the spread of the disturbance. The
where
distributions of the test quantity under each hypothesis, p(q|H0 ) and p(q|H1 ),
are estimated using a kernel density estimator based on Monte Carlo simulations. They are shown for different levels of temperature disturbances ∆ξ in Figures 4.8a to 4.8c. As can be seen, the overlap between the hypotheses densities
increases with ∆ξ , complicating a decision. The probabilities of error are shown
in Figure 4.8d for different values of the threshold, illustrating that performance
can considerably vary depending on the disturbances for any given threshold
choice.
4.3 Decision Rule
49
p(q|H0 )
p(q|H1 )
1500
p(q|H0 )
p(q|H1 )
600
1000
400
200
500
200
100
0
0
5 s 10
Pf , Pm
(a) ∆ξ = 6◦ C.
15
×10−3
0
1
0.8
0.6
0.4
0.2
0
0
0
5 s 10
0
15
×10−3
(b) ∆ξ = 10◦ C.
p(q|H0 )
p(q|H1 )
300
0
5 s 10
(c) ∆ξ = 20◦ C.
15
×10−3
∆ξ = 6◦ C
∆ξ = 10◦ C
∆ξ = 20◦ C
2
4
~
6
8
(d) Pf (solid) and Pm (dashed).
×10−3
Figure 4.8: Kernel density estimates for the hypotheses’ densities under different levels of temperature disturbance ∆ξ (top row). The probabilities of
error as a function of the threshold ~ (bottom).
4.3.2 Likelihood ratio tests
As depicted by (4.27), the performance for a decision rule is determined by the
statistical behavior of the test quantity under the different hypotheses, described
by (4.25). In case these models are known, it is thus natural to consider them
when defining the decision rule. In this direction, the following result is fundamental.
Lemma 4.1 (Neyman-Pearson). Consider the hypotheses given by (4.25). Let
the likelihood ratio function be defined as
Λ(q) ,
p 1 (q)
.
p 0 (q)
For ~ ≥ 0, define the acceptance region
R0 = {Λ(q) ≤ ~} ,
with error probabilities
Pf =
Z∞
0
p(Λ | H ) dΛ,
~
Let another acceptance region
R′0
Pm =
Z~
−∞
(4.29)
p(Λ | H1 ) dΛ.
(4.30)
with error probabilities Pf′ and Pm′ . If Pf′ ≤ Pf
4 Basics of Fault Diagnosis
50
then Pm′ ≥ Pm . Additionally, if Pm′ ≤ Pm then Pf′ ≥ Pf .
Proof: See Theorem 11.7.1 in Cover and Thomas (2006).
The test based on the likelihood ratio in (4.29) is therefore optimal according to
to a Neyman-Pearson criterion. Notice that the threshold ~ can be found for a
Neyman-Pearson criterion by solving either of the equations in (4.30) for a prespecified left hand side.
Generalized likelihood ratio tests
In case the hypotheses densities are described by unknown parameters,
H0 : q ∼ p(q | θ0 ),
H1 : q ∼ p(q | θ1 ),
there is not a general test that is optimal in a Neyman-Pearson sense. A natural
approximation is to first find the maximum likelihood estimate of the parameters
and use the resulting likelihood ratio, i.e.,
max p 1 (q | θ1 )
θ1
b
.
Λ(q)
=
max p 0 (q | θ0 )
0
θ
b
The test with acceptance region given by R0 = {Λ(q)
≤ ~} is called a generalized
likelihood ratio test (glr). Asymptotic optimality conditions (when the number
of observations of the test quantity goes to infinity) of the generalized likelihood
ratio test are studied in Zeitouni et al. (1992).
4.3.3 Statistical significance tests
In order to evaluate likelihood ratio tests, the distribution functions p 1 (q) and
p 0 (q) (or their parametric description) must be known. In practice, this is often
not possible, particularly for the alternative hypothesis H1 which describes abnormal behaviors which are typically unknown.
It is possible to define an acceptance region, R0 , based only on a model for the
null hypothesis H0 . For a pre-defined Pf′ , the acceptance region can be found as
the solution to
Z
′
Pf = 1 − p 0 (q) dq.
R0
The resulting acceptance region tests whether the observations are consistent to
its nominal behavior. There is not a unique R0 that satisfies this criterion. For
example, a decision region defined over the entire space except for a value q∗ ,
i.e., R0 = R − q∗ , gives arbitrarily small Pf for any choice of q∗ . It is common to
consider R0 as the smallest continuous interval in the observation space, when
the decision region is known as the confidence interval. This type of decision rule
was used in Example 4.4 to test for significant changes in parameter values.
4.3 Decision Rule
51
1
Pd
0.8
0.6
0.4
N =1
N =5
N = 10
0.2 0.3 0.4 0.5
Pf
0.2
0
0
0.1
Figure 4.9: roc curves illustrating the compromise between detection error
and sample size (detection time).
4.3.4 Compromises between errors and time of detection
Even optimal likelihood ratio tests may not achieve the performance requirements for a given application. The performance can be improved if it is possible
to consider more data before making a decision. This is more easily described
with an example.
Example 4.8: Likelihood ratio test for a change in the mean of a Gaussian
Consider that N observations of the test quantity are available to perform the
decision, i.e., q ∈ RN . As an example, take the following hypotheses
H0 : q ∼ N (0, I ),
H1 : q ∼ N (1, I ),
(4.31)
the acceptance region given by the likelihood ratio for this problem is
T
1
1
T
|2πI |−1/2 e − 2 (q−1) (q−1)
= e − 2 (−2q 1+N ) ≤ ~.
Λ(q) =
1
T
−1/2 − 2 q q
|2πI |
e
Taking the logarithm (a monotonic transformation) does not affect the inequality
and isolating q on the left hand side gives the equivalent acceptance region
s,
N
X
n=1
q n ≤ ~′ ,
~′ .
for a new threshold
The quantity s is a sufficient statistic for the test since
it describes it completely. According to (4.31), the distribution of s under the
different hypotheses are
p(s | H0 ) = N (s; 0, N ),
and the probabilities of error are
Pf =
Z∞
~′
1
e
√
2πN
2
s
− 2N
ds,
p(s | H1 ) = N (s; N , N )
Pm = 1 − Pd = 1 −
Z∞
~′
(s−N )2
1
e − 2N ds
√
2πN
which are functions of the number of observations N . By varying the threshold
52
4 Basics of Fault Diagnosis
~′ from −∞ to ∞ and plotting Pf against Pd , a receiver operating characteristic
(roc) curve is achieved and can be used to evaluate decision rules. roc curves are
displayed for different values of N in Figure 4.9 where the effects of the sample
size is clear.
As illustrated by the example, increasing the number of observations can lead to
an improved detection performance. The shortcoming is that an accurate enough
decision will be postponed before enough data is collected. That is, an accurate
enough decision is compromised with the detection time.
Heuristic approaches can also be used to improve the detection errors. For instance, the test quantity can be low pass filtered before input to the decision rule
or a decision can be made after the test quantity exceeds the threshold for a number of times consecutively. These will however introduce delay to the detection
(Adnan et al., 2011).
4.4 Summary and Connections
This chapter presented an overview of the diagnosis process. The presentation
focused on describing different fault detection methods, with special attention
to methods that are suitable to the main application considered, i.e., detecting
friction and wear changes in industrial robots. A number of examples were presented to illustrate relevant methods and trade-offs.
Gathering knowledge about the disturbances and faults is an important aspect
for the design and verification of fault detection methods. The achieved friction
models from Papers A and B allow for a more realistic design and evaluation of
solutions to wear diagnosis in industrial robot joints. These models were used in
Example 4.7.
An off-line active method for wear monitoring is proposed in Paper B. The method
is based on the maximum likelihood estimation of a wear related quantity and
makes use of the developed friction models and a dedicated test-cycle. Experiment design is considered in detail as to achieve as accurate estimate of wear as
possible for a limited experiment time.
In Paper C, distribution domain methods are proposed for fault detection and
isolation of repetitive systems. Changes in the distribution of data collected from
a repetitive operation are monitored by test quantities similar to that of (4.23).
In order to reduce sensitivity of the test quantities to disturbances, an approach
is suggested based on the use of a weighting function which is found based on a
criterion closely related to the Fisher linear discriminant in (4.23). Improvements
in detection performance achieved with the use of the weights is illustrated based
on simulations, in a similar approach as presented in Example 4.7.
The measures of performance defined in Section 4.3 are also used in Paper D
when defining an approach to simulation based evaluation and comparison of
fault detection algorithms.
4.4 Summary and Connections
53
Paper E proposes studies the design of decision rules that only require nominal
data of the test quantity. The approach is divided in two steps. First, a nonparametric density model for the nominal behavior of the test quantity, b
p 0 (q) is
found based on the available data. Second, generalized likelihood ratio tests are
defined based on the assumption that the alternative hypothesis is described by
b
p 0 (q | ∆) = b
p 0 (q − ∆), i.e., under the assumption that a fault will appear as a
bias change. The unknown change size ∆ is found based on maximum likelihood
estimates.
5
Conclusions and Discussion
The first part provided an introduction to the research fields that are relevant for
this thesis: industrial robotics, tribology and diagnosis. This served as a preparation to Part II, motivating the research contributions and contextualizing them.
The conclusions for Part I that relate to the research goals of the thesis are given
in Section 5.1. The discussion is followed by a summary of the research contributions of Part II, overall conclusions of the thesis in Section 5.3 and recommendations of future research in Section 5.4. See also the included papers for details.
5.1 Conclusions of Part I
As presented in Chapter 2, there are different aspects of industrial robotics that
challenge the research goals of this thesis.
Complex dynamics. Industrial robots are nonlinear, multi-variable, uncertain
systems operating in closed-loop.
Limited sensory information. In a typical setup, only motor angular positions
ϕ m , and applied motor currents i m are measured. From these measurements, estimates of angular speeds ϕ̇ m and the applied motor torques τ m
are possible.
Application-related limitations. Industrial robots are used in a wide range of
applications. Depending on the installation, there will be restrictions on
the available workspace and on how the robot can be used.
For various reasons, the use of models is important in industrial robotics. Due
to the complexity of a robot joint, models describing, e.g., backlash and friction
are difficult and are often motivated from empirical observations. With interest
55
56
5 Conclusions and Discussion
in the determination of critical wear changes based on its effect to friction, Chapter 3 presented an overview of friction in a robot joint. Important characteristics
of friction and wear that should be considered when designing solutions for diagnosis are:
Small fault to disturbance ratio. The effects of temperature and load variations
to friction are comparable, often larger, than the effects related to wear.
Complex and individual behavior. The friction behavior is determined by complex interactions at a surface level. As a result, although a similar qualitative behavior may be observed for different gearbox-motor pairs, its quantitative behavior will differ in general.
Lubricant. The use of different lubricants will considerably affect friction as well
as its temperature dependencies. It is thus important to keep track of possible changes of lubricant and its condition.
Unpredictability of the wear processes. The effects of wear to friction will depend on how the wear takes place in the mechanical parts of the joint which
are in general difficult to predict and determine.
Chapter 4 gave an overview of fault diagnosis and some of the challenges involved. In fault diagnosis, observations and available knowledge of the system
are compared to infer the state present in the system. The knowledge embedded in the diagnosis solution can take different forms, e.g., a system model or
expert knowledge of signals and data. The type of knowledge representation
available will affect the design and application requirements. Since only limited
knowledge can be embedded in a diagnosis solution, the following factors are
important to consider.
Evaluation. Methods should be evaluated extensively based on real data as much
as possible and complemented with realistic laboratory and simulation studies.
Detection compromises. A correct determination of fault presence cannot be
performed with arbitrary accuracy and promptness and a compromise must
be made.
All of these aspects will delimit the applicability of possible solutions and should
therefore be considered before deployment.
5.2 Summary and Discussion for Part II
Paper A: Friction in a Robot Joint – Modeling and Identification of Load and
Temperature Effects
In Paper A, a detailed empirical study of friction in a robot joint is presented.
The study was motivated by the complexity of friction in a robot (recall Figure
3.2). In the paper, the typical friction related phenomena and models used in
robotics are reviewed. The effects of angular position, speed, load torques and
5.2 Summary and Discussion for Part II
57
temperature to friction in a robot joint are considered. Due to their relevance,
a model to describe the effects of speed, load and temperature is suggested and
validated. The proposed model considerably outperforms standard friction models and is important for design and evaluation of diagnosis methods but also for
control and simulation. The requirements for the identification of the complete
friction model are described below, together with a description of the suggested
approaches for their accomplishment.
Rich friction data. Friction data collected under large variations of load, temperature and speed are needed in order to accurately find the parameters.
A simple and short experiment is suggested for the collection of a friction
datum at a given speed value. The experiment can be repeated for different
temperature, load and speed conditions until enough data are collected to
accurately estimate the parameters.
As suggested in the paper, variations of load can be achieved by performing
the experiment in different configurations of the robot but will require a
large region of the robot workspace. Variations of temperature are more
difficult since the thermal constant in a robot joint is of several hours.
Temperature and load estimates. Estimates or measurements of the joint temperature and of load torques are needed in order to find the associated friction parameters.
An estimate for the load torques is possible based on a robot model. In
the experiments, temperature was measured with a temperature probe immersed in the gearbox lubricant oil.
The requirements for the identification procedure will possibly exceed the constraints posed by the robot user since extra sensory information, a large region
of the workspace and a long experiment time are needed. For these reasons, the
procedure is better suited for laboratory studies. For instance, the model can be
found during commissioning of newly produced robots.
It is worth mentioning that alternatives to lubricant temperature measurements
are possible. For instance, it is possible to find an estimate based on availability
of environment and housing temperature sensors and a thermal model for the
joint as described by Marton and van der Linden (2012). However, in order to
find the thermal model for the joint, readings of the lubricant temperature are
also needed.
Paper B: Modeling and Experiment Design for Identification of Wear in a Robot
Joint Under Load and Temperature Uncertainties
In Paper B, the effects of wear to friction are modeled and identified. The model is
based on empirical studies from accelerated wear tests performed in a robot joint.
This model is combined with the friction model suggested in Paper A to achieve
a description of friction as function of speed, temperature, load and wear. Using
the resulting model, a method is proposed for wear monitoring. The method
58
5 Conclusions and Discussion
is based on the estimation of a wear related quantity from friction data and is
formulated as a maximum likelihood problem.
To study the applicability of parameter estimation as a solution for wear monitoring, a simplified but realistic scenario is considered with availability of a friction
model and friction data. These are the requirements for the method and, as suggested in the paper, they can be achieved as follows.
Estimates of load torques. It is considered that an uncertain estimate of the load
torques at the joints is available. This can be achieved with the use of a robot
model.
Friction model parameters related to temperature and load. These parameters
describe the nominal behavior of friction and can be found based on laboratory experiments, as suggested for Paper A.
Friction model parameters related to the effects of wear. The effects of wear to
friction will depend on the behavior of the fault, which can not be known
a priori. To circumvent this, it is suggested that these effects are studied
and modeled based on historical failure data, e.g., collected from accelerated wear tests. From such historical data, parameters can be found for the
model structure suggested in the paper or, if needed, an alternative model
structure can be developed that is better suited for the observed phenomena.
Friction data. The same experiment suggested for Paper A to collect a friction
datum for a certain speed level can be used.
Imposed by the total allowed experiment time, there will be a fixed number
of friction data points that can be be collected. To achieve as accurate as
possible estimate of the wear quantity, a criterion for experiment design is
used to select the speed values that will lead to a reduced uncertainty for
the wear estimate. More accurately, the criterion aims at minimizing the
Cramér-Rao lower bound for the covariance of unbiased wear estimators.
The approach is validated with special attention to limitations imposed by temperature and load. As it is shown from simulations and real data studies, for an
experiment time constrained to less than one minute for the entire robot, it is
possible to accurately estimate the wear quantity despite variations of load and
temperature. This was possible even in scenarios where the parameters related
to a wear fault were not optimized for the faults taking place. An important advantage with the approach is that the estimated wear parameter directly relates
to effects of wear to friction and therefore allows for a natural interpretation.
Paper C: A Data-driven Approach to Diagnostics of Repetitive Processes in the
Distribution Domain
In Paper C, data-driven methods are proposed for diagnosis of systems that behave in a repetitive manner. The basic idea is to compare data collected in different instances of time from a repetitive behavior to provide an estimate of how
5.2 Summary and Discussion for Part II
59
the system changed over the period.
A data-driven method is suggested where the distribution of incoming data is
compared to the distribution of data collected under nominal conditions. A kernel density estimator is used, in combination with the Kullback-Leibler distance.
An approach is also suggested to reduce sensitivity to disturbances with the use
of a weighting function. The requirements for the methods are given below.
Data from a repetitive operation. A repetitive operation is commonly found in
many applications, e,g, for industrial robots and in automated manufacturing. A repetitive behavior can also be forced with the execution of a
diagnostic routine.
Nominal data. Data collected from the system under nominal conditions are
needed. For example, these data can be collected from the system when
it is new.
Weighting function (optional). As suggested in the paper, data can be weighted
according to a weighting function to reduce sensitivity to disturbances.
A procedure is suggested to find the weights based on a criterion similar
to that found for Fisher discriminant analysis. The procedure requires
availability of fault-free as well as faulty data. In the paper, such data are
achieved from simulations.
The methods are illustrated for the problems of wear monitoring in industrial
robots and for the diagnosis of gear faults in a gearbox system. For the robotics
problem, the effects of temperature are studied based on simulations, where it is
shown that the use of the weighting function can considerably reduce sensitivity
to temperature variations. An important advantage of the approach is that no
models are needed and it is also simple to implement.
Paper D: Simulation based Evaluation of Fault Detection Algorithms
In Paper D, a framework is suggested for the evaluation of fault detection algorithms. The effects of faults and disturbances to the test quantities are studied
and approaches are proposed in an attempt to determine,
i ) which disturbances affect a method the most,
ii ) which methods perform better in average to indicate presence of a fault,
iii ) which combinations of fault change size and variations of disturbances are
allowed to achieve satisfactory performance.
A simulation based approach is suggested since it allows for a more comprehensive study than it is often possible based on real data. This is particularly critical
for studies of wear faults in industrial robots since wear tests are both cost and
time critical.
Even with simulation data, some studies may take prohibitive amounts of time.
An important idea suggested in the paper is to bypass the need for data by mak-
60
5 Conclusions and Discussion
ing a direct map from faults and disturbances to the test quantity. Such direct
map is known as a surrogate model and includes both the simulator and fault
detection algorithm. The advantage is that, once the surrogate models are found
and validated, they can be used to calculate the response of the test quantities for
different values of faults and disturbances in a very short time, thus allowing for
extensive Monte Carlo simulations. For example, one of the studies in the paper
was evaluated in twelve seconds with the surrogate models. The same study performed based on simulations of the entire system would have taken more than
three years.
The use of the proposed approaches is illustrated for the problem of wear monitoring in industrial robots where three fault detection algorithms are evaluated. The
study reveals interesting properties of the problem and of the candidate methods.
For instance, it shows that variations of temperature significantly affect all methods considered. It also shows how much variations of load and temperature are
allowed to clearly detect a wear fault of a certain size, revealing the scope of their
applicability. Finally, it also gives a criterion for selection of the fault detection
algorithm that performs best in average.
It should be stressed that conclusions drawn based on simulations or surrogate
models should always be carried out carefully since these are a limited representation of reality. Nevertheless, this type of study can reveal valuable properties
of the problem at hand and of the fault detection methods. For instance, they
can be used to justify choices regarding further developments and selection of
candidate solutions.
Paper E: Data-Driven Anomaly Detection based on a Bias Change
Paper E considers the problem of determination of a decision rule which,
i ) requires minimal and meaningful specification parameters from the user,
ii ) is flexible and can be used for different problems,
iii ) can provide estimates for the probabilities of error in the decision.
The suggested approach only requires availability of nominal data of the test
quantity, which is a common situation in practice. The user only needs to specify
a desired probability of false detection, which is a natural design choice. No
model or other parameters need to be pre-determined.
The suggested decision rule is achieved in two steps based on approximate models for the statistical behavior of the test quantity. First, the nominal data are
used to find a density model to describe the nominal behavior (the null hypothesis) of the test quantity, b
p 0 (q). Non-parametric density models are used, giving
the required flexibility.
In order to provide estimates of the probability of miss as in (4.27b), a statistical
model for the behavior of the test quantity under presence of a change (the alternative hypothesis) is needed. An assumption is introduced that presumes that
5.3 Conclusions
61
an abnormality will affect the test quantity as a bias change relative to its nominal statistical behavior, i.e., b
p 1 (q) = b
p 0 (q − ∆), which is a simple and intuitive
model. The unknown change size ∆ is estimated from incoming test data based
on suggested maximum likelihood estimators.
The resulting models b
p 0 (q) and b
p 1 (q) = b
p 0 (q − b
∆) are then used to define generalized likelihood ratio tests assuming that these are the true models. Batch
and sequential approaches are suggested which are important depending on the
application sought. The proposed decision rule is illustrated for real data problems as well as simulations. When applied to the wear monitoring problem, it is
shown how an early detection can be made with the suggested decision rule. It
also provides estimates of the decision uncertainties, which are useful to support
higher level service decisions.
5.3 Conclusions
Returning to the main research goals of the thesis,
Design and investigate the applicability of methods to detect critical
changes of wear based on standard sensory information and limited
intervention with the system operation to support service.
Considering the achieved results, the following conclusions are made.
Understanding. The studies and modeling of friction and wear presented in Papers A and B contribute to the overall understanding of the phenomena.
Without these studies it would have been difficult to verify the applicability of the proposed solutions. Beyond the examples reported in the thesis,
similar phenomena were observed for several robot units from studies carried out in field, laboratory and production environments, increasing the
confidence in the generalization of the studies.
Design. Methods for wear monitoring were suggested in Paper B based on the
identification of a wear quantity and in Paper C based on a data-driven approach. Both are based on standard sensory information but have different
requirements as discussed in the previous section.
The method based on the estimation of a wear parameter requires availability of a friction model as well as friction data which can be collected based
on an experiment that takes one minute or less. The data-driven approach
does not require a friction model and is very simple to implement but requires nominal data and the achieved test quantity does not carry a natural
interpretation as for the wear estimates. In case a repetitive operation can
be found during normal operation, the data-driven approach does not require dedicated experiments. Otherwise, repetitive data can be collected
based on an experiment of similar length as for the wear estimation.
Applicability. The suggested methods for wear monitoring were evaluated with
success based on real data as well as simulations in realistic scenarios in
62
5 Conclusions and Discussion
Papers B and C. The simulation based evaluation framework suggested in
Paper D can also serve as a tool to investigate the applicability of the methods.
The methods were also successfully evaluated in a number of additional
accelerated wear tests, increasing confidence in their applicability. However, it would be advisable to extend the verification based on more studies
carried out in the field.
Furthermore, because the quantitative behavior of friction in a tribological
system is rather individual, changes affecting it, such as the replacement
of the lubricant or components in the joint need to be considered as they
can affect the methods. For instance, they may require an update of the
parameters for the friction model or the assignment of new nominal data.
Tools to support service. The methods for wear monitoring proposed in Papers
B and C are important tools to support the service of industrial robots. The
methods presented in Paper E also provide the service engineer with tools
for an automatic determination of fault presence. The evaluation framework of Paper D provides valuable tools to support the design, evaluation
and comparison of different methods.
The research goals of this thesis have therefore been addressed in multiple fronts.
5.4 Recommendations for Future Research
Several further developments are suggested in the end of each paper in Part II.
Besides those, the following aspects are emphasized.
Understanding and modeling of friction. More extensive studies of friction will
support further design and evaluation of possible solutions.
• Modeling of dynamic friction. Only static friction models were developed
in this work. One possibility is to verify whether the static friction models
developed can be extended to a dynamic description, e.g., based on the
LuGre model.
• Validation over larger temperature ranges. The temperature effects were
modeled in a temperature range of 35−80◦ C. Robots may operate outside
this range. It is therefore important to understand how friction would be
affected.
• Verification of the developed models in other mechanical devices. Realistic
friction models are important in many applications. The effects of load and
temperature are often neglected, despite their importance. In principle,
the developed models can be extended to other types of mechanical devices
under lubricated friction. The use of the model structure proposed would
simplify the time consuming task of developing new friction models.
5.4 Recommendations for Future Research
63
For instance, while the study of friction considered here focused on the
main axes of the robot, it would be interesting to study friction also in the
wrist axes.
Understanding and modeling of wear. Since it is costly and time consuming to
perform wear experiments, wear models are very important for the design and
verification of diagnosis solutions. The further study of wear and wear modeling
in a robot joint is motivated by the following.
• Unpredictability of the wear processes. It is not possible to assure that a
wear model developed based on an observed fault is representative of future faults. The wear processes in a joint are the results of complex phenomena that cannot be easily predicted. With more knowledge gathered
about the faults, it is possible to describe them in a more detailed manner. Perhaps different wear models can be developed to describe the most
common faulty behaviors. This would be useful for instance for the wear
estimation method proposed in Paper B.
• Development of lifetime models. Understanding how wear evolves with
time and usage is important for the design and verification of diagnosis
solutions. For example, it supports the scheduling of diagnostic tests and
the design of decision rules.
A lifetime model also allows for prognosis, which is very important for the
scheduling of maintenance routines.
Design of alternative solutions. It would be interesting to investigate alternative solutions for wear monitoring with reduced requirements and/or improved
accuracy and reliability.
• Monitoring based on operational data. The methods suggested in the thesis
are, in general terms, better suited for the scenario where it is acceptable to
perform dedicated experiments for diagnosis. Although these experiments
are short in time and in the required use of the workspace, it would be
interesting to further investigate methods where no dedicated experiments
are needed. The applicability of such methods will however depend on how
the system is being used. Furthermore, additional uncertainties may also
appear, e.g., related to unmodeled flexible dynamics of the robot, and the
evaluation of such methods in realistic scenarios is thus difficult.
Evaluation of the methods. It is important to thoroughly evaluate the approaches
before use in real world applications. This will help to identify weaknesses and
will provide leads to possible improvements. Three approaches are listed.
• Simulation studies. With the use of the developed friction models, it is
possible to setup more realistic simulation models. In simulation studies,
it is important to consider the different sources of uncertainties present in
practice, e.g., flexibilities, torque ripple, temperature and load variations,
closed-loop effects, etc. For on-line methods, trajectories that are used in
real world applications should be considered.
64
5 Conclusions and Discussion
To allow for a comparison of different methods, a benchmark problem for
robust wear diagnosis in a robot joint could be defined using such a model.
• Accelerated wear tests. Even though a realistic simulation model is important, it cannot substitute the validation through experiments. Since the
wear effects may take several years to occur, accelerated wear tests, performed in a lab, can be used as a first verification. It is however difficult
to reproduce scenarios that are representative for what happens in the field.
For example, it is difficult to control temperature in the joints.
• Field tests. These are irreplaceable for the evaluation of diagnosis solutions.
In order to be of significance, they must be verified with several robots and
in different applications. This is however only possible in cooperation with
robot users and can take considerable time.
Bibliography
N. Adnan, I. Izadi, and T. Chen. Computing detection delays in industrial alarm
systems. In Proceedings of the 2011 American Control Conference, pages 786
–791, July 2011.
D. Agarwal. Detecting anomalies in cross-classified streams: a Bayesian approach.
Knowledge and Information Systems, 11(1):29–44, 2007.
F. Al-Bender and J. Swevers. Characterization of friction force dynamics. IEEE
Control Systems Magazine, 28(6):64–81, 2008.
T. Ardeshiri, M. Norrlöf, J. Löfberg, and A. Hansson. Convex optimization approach for time-optimal path tracking of robots with speed dependent constraints. In Proceedings of 18th IFAC World Congress, pages 14648–14653,
Milano, Italy, September 2011.
K. J. Åström and C. Canudas-de Wit. Revisiting the LuGre friction model. IEEE
Control Systems Magazine, 28(6):101–114, December 2008.
P. Axelsson. Sensor Fusion and Control Applied to Industrial Manipulators. Linköping Studies in Science and Technology. Dissertations No. 1585, Linköping
University, SE-581 83 Linköping, Sweden, May 2014.
M. Basseville. Distance measures for signal processing and pattern recognition.
Signal Processing, 18(4):349–369, 1989.
M. Basseville and I. V. Nikiforov. Detection of abrupt changes: theory and application. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1993.
A. C. Bittencourt and P. Axelsson. Modeling and experiment design for identification of wear in a robot joint under load and temperature uncertainties based
on friction data. IEEE/ASME Transactions on Mechatronics, 19(5):1694–1706,
October 2014.
A. C. Bittencourt and S. Gunnarsson. Static friction in a robot joint— Modeling
and identification of load and temperature effects. Journal of Dynamic Systems, Measurement, and Control, 134(5), July 2012.
65
66
Bibliography
A. C. Bittencourt and T. Schön. Data-driven anomaly detection based on a bias
change. In Proceedings of the 19th IFAC World Congress, Cape Town, South
Africa, 2014.
A. C. Bittencourt, K. Saarinen, S. Sander-Tavallaey, S. Gunnarsson, and M. Norrlöf. A data-driven approach to diagnostics of repetitive processes in the distribution domain – Applications to gearbox diagnostics in industrial robots and
rotating machines. Mechatronics, -(0):–, 2014. available online.
P. J. Blau. Embedding wear models into friction models. Tribology Letters, 34(1),
April 2009.
L. C. Bo and D. Pavelescu. The friction-speed relation and its influence on the
critical velocity of stick-slip motion. Wear, 82(3):277–289, 1982.
B. Bona and M. Indri. Friction compensation in robotics: an overview. In Proceedings of the 44th IEEE International Conference on Decision and Control,
Seville, Spain, December 2005.
A. W. Bowman and A. Azzalini. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science
Series). Oxford University Press, USA, November 1997.
D. Brambilla, L. Capisani, A. Ferrara, and P. Pisu. Fault detection for robot manipulators via second-order sliding modes. IEEE Transactions on Industrial
Electronics, 55(11):3954–3963, November 2008.
T. Brogårdh. Present and future robot control development - an industrial perspective. Annual Reviews in Control, 31:69–79, 2007.
T. Brogårdh. Robot Control Overview: An Industrial Perspective. Modeling,
Identification and Control, 30(3):167–180, 2009.
M. D. Bryant. Entropy and dissipative processes of friction and wear. FME Transactions, 37(2):55–60, 2009.
F. Caccavale, P. Cilibrizzi, F. Pierri, and L. Villani. Actuators fault diagnosis for
robot manipulators with uncertain model. Control Engineering Practice, 17(1):
146–157, 2009.
T. Cacoullos. Estimation of a multivariate density. Annals of the Institute of
Statistical Mathematics, 18:179–189, 1966.
M.-O. Cordier, P. Dague, F. Levy, J. Montmain, M. Staroswiecki, and L. TraveMassuyes. Conflicts versus analytical redundancy relations: a comparative
analysis of the model based diagnosis approach from the artificial intelligence
and automatic control perspectives. IEEE Transactions on Systems, Man, and
Cybernetics, Part B: Cybernetics, 34(5):2163–2177, Oct 2004. ISSN 1083-4419.
doi: 10.1109/TSMCB.2004.835010.
T. M. Cover and J. A. Thomas.
interscience, 2006.
Elements of information theory.
Wiley-
Bibliography
67
J. De Kleer and B. C. Williams. Diagnosing multiple faults. Artificial intelligence,
32(1):97–130, 1987.
C. W. de Silva. Vibration Monitoring, Testing and Instrumentation. CRC Press,
April 2007.
M. Desforges, P. Jacob, and A. Ball. Fault detection in rotating machinery using
kernel-based probability density estimation. International Journal of Systems
Science, 31(11):1411–1426, 2000.
S. X. Ding. Model-Based Fault-Diagnosis Techniques - Design Schemes, Algorithms and Tools. Springer, 2nd edition, 2013.
D. Dowson. History of Tribology. Professional Engineering Publishing, London,
UK., 2nd edition, 1998.
P. Dupont, V. Hayward, B. Armstrong, and F. Altpeter. Single state elastoplastic
friction models. IEEE Transactions on Automatic Control, 47(5):787 –792, May
2002.
R. Durrett. Probability: Theory and examples. Cambridge University Press, August 2010.
P. Frank and X. Ding. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. Journal of Process Control, 7(6):
403–424, 1997.
B. Freyermuth. An approach to model based fault diagnosis of industrial robots.
In Proceedings of the 1991 IEEE International Conference on Robotics and Automation, volume 2, pages 1350–1356, April 1991.
J. H. Friedman. Regularized discriminant analysis. Journal of the American statistical association, 84(405):165–175, 1989.
M. Gautier and P. Poignet. Extended Kalman filtering and weighted least squares
dynamic identification of robot. Control Engineering Practice, 9(12):1361 –
1372, 2001.
A. Gogoussis and M. Donath. Coulomb friction effects on the dynamics of bearings and transmissions in precision robot mechanisms. In Proceedings of the
1998 IEEE International Conference on Robotics and Automation, pages 1440–
1446, Leuven, Belgium, April 1988.
Y. Guo, Z. Qu, Y. Braiman, Z. Zhang, and J. Barhen. Nanotribology and nanoscale
friction. IEEE Control Systems Magazine, 28(6):92–100, December 2008.
F. Gustafsson. Adaptive Filtering and Change Detection. Wiley, October 2000.
P. Hamon, M. Gautier, and P. Garrec. Dynamic identification of robots with a
dry friction model depending on load and velocity. In Proceedings of the 2010
IEEE/RSJ International Conference on Intelligent Robots and Systems, pages
6187–6193, Taipei, Taiwan, October 2010.
68
Bibliography
A. Harnoy, B. Friedland, and S. Cohn. Modeling and measuring friction effects.
IEEE Control Systems Magazine, 28(6), December 2008.
J. M. Hollerbach. A survey of kinematic calibration, pages 207–242. MIT Press,
Cambridge, MA, USA, 1989.
A. Isaksson, A. C. Bittencourt, K. Forsman, and D. Peretzki. Method for controlling an industrial process, October 2010. URL http://patentscope.
wipo.int/search/en/WO2012048734.
R. Isermann. Fault-Diagnosis Systems - An Introduction from Fault Detection to
Fault Tolerance. Springer, 1st edition, 2006.
ISO 8373. Manipulating industrial robots – Vocabulary. ISO, Geneva, Switzerland, 1994.
B. Jacobson. The Stribeck memorial lecture. Tribology International, 36(11):781–
789, 2003.
T. M. Jahns and W. L. Soong. Pulsating torque minimization techniques for permanent magnet AC motor drives – a review. IEEE Transactions on Industrial
Electronics, 43(2):321–330, 1996.
M. Jones and D. Henderson. Maximum likelihood kernel density estimation: On
the potential of convolution sieves. Computational Statistics & Data Analysis,
53(10):3726–3733, 2009.
M. Jonsson, A. Stolt, A. Robertsson, S. von Gegerfelt, and K. Nilsson. On force
control for assembly and deburring of castings. Production Engineering, 7(4):
351–360, 2013.
F. I. Khan and S. A. Abbasi. Major accidents in process industries and an analysis
of causes and consequences. Journal of Loss Prevention in the Process Industries, 12(5):361–378, 1999.
H. M. Kim, S. H. Park, and S. I. Han. Precise friction control for the nonlinear
friction system using the friction state observer and sliding mode control with
recurrent fuzzy neural networks. Mechatronics, 19(6):805–815, 2009.
S. Kotsiantis. Supervised machine learning: A review of classification techniques.
Informatica, 31:249–268, 2007.
A. R. Lansdown, A. L. Price, and J. Larsen-Basse. Materials to resist wear – a
guide to their selection and use. Journal of Tribology, 109(2):379–380, 1987.
N. Liu and K. Zhou. Optimal robust fault detection for linear discrete time systems. Journal of Control Science and Engineering, 7:1–16, January 2008.
L. Ljung. System Identification: Theory for the User. Prentice Hall PTR, 2nd
edition, December 1999.
J. Machalíková, M. Sejkorová, J. Chýlková, and E. Schmidová. Application of tribodiagnostics in the maintenance of vehicles. In Proceedings of the 5th Inter-
Bibliography
69
national Conference on Theoretical and Practical Issues in Transport, February
2010.
L. Marton and F. van der Linden. Temperature dependent friction estimation:
Application to lubricant health monitoring. Mechatronics, 22(8):1078–1084,
2012.
R. Mattone and A. D. Luca. Relaxed fault detection and isolation: An application
to a nonlinear case study. Automatica, 42(1):109 – 116, 2009.
M. McIntyre, W. Dixon, D. Dawson, and I. Walker. Fault identification for robot
manipulators. IEEE Transactions on Robotics, 21(5):1028–1034, October 2005.
S. Moberg. Modeling and Control of Flexible Manipulators. Linköping studies in
science and technology. Dissertations, no. 1349, Linköping Studies in Science
and Technology, SE-581 83 Linköping, Sweden, December 2010.
Y.-R. Mohamed and E. El-Saadany. A current control scheme with an adaptive
internal model for torque ripple minimization and robust current regulation
in PMSM drive systems. IEEE Transactions on Energy Conversion, 23(1):92
–100, March 2008.
M. Namvar and F. Aghili. Failure detection and isolation in robotic manipulators
using joint torque sensors. Robotica, 2009.
H. Olsson, K. J. Åström, C. C. de Wit, M. Gafvert, and P. Lischinsky. Friction
models and friction compensation. European Journal of Control, 4(3):176–195,
1998.
E. Parzen. On estimation of a probability density function and mode. The Annals
of Mathematical Statistics, 33(3):1065–1076, 1962.
D. Peretzki, A. J. Isaksson, A. C. Bittencourt, and K. Forsman. Data mining of historic data for process identification. In Proceedings of the 2011 AIChE Annual
Meeting, October 2011.
A. Proca, A. Keyhani, A. El-Antably, W. Lu, and M. Dai. Analytical model for
permanent magnet motors with surface mounted magnets. IEEE Transactions
on Energy Conversion, 18(3):386 – 391, September 2003.
B. K. N. Rao. Condition monitoring and the integrity of industrial systems. In
A. Davies, editor, Part 1: Introduction to Condition Monitoring, Handbook of
Condition Monitoring – Techniques and Methodology, chapter 1, pages 3–34.
Chapman & Hall, London, UK, 1998.
M. D. Reid and R. C. Williamson. Information, divergence and risk for binary
experiments. Journal of Machine Learning Research, 12:731–817, 2011.
R. Reiter. A theory of diagnosis from first principles. Artificial Intelligence, 32
(1):57 – 95, 1987. ISSN 0004-3702.
B. Roylance. Ferrography – then and now. Tribology International, 38(10):857 –
862, 2005.
70
Bibliography
P. Rzeszucinski, J. Sinha, R. Edwards, A. Starr, and B. Allen. Amplitude of probability density function (APDF) of vibration response as a robust tool for gearbox diagnosis. Strain, 48(6):510–516, 2012.
A. Samuelsson, A. C. Bittencourt, K. Saarinen, S. S. Tavallaey, M. Norrlöf, H. Andersson, and S. Gunnarsson. Simulation based evaluation of fault detection
algorithms with applications to wear diagnosis in manipulators. In Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, 2014.
S. Sander-Tavallaey and K. Saarinen. Backlash identification in transmission unit.
In Proceedings of the 2009 IEEE Control Applications & Intelligent Control,
pages 1325 –1331, July 2009. doi: 10.1109/CCA.2009.5281173.
S. Sander-Tavallaey, K. Saarinen, H. Andersson, and A. C. Bittencourt. Condition monitoring of an industrial robot, October 2012. URL http://
patentscope.wipo.int/search/en/WO2013050314.
L. Sciavicco and B. Siciliano. Modelling and control of robot manipulators. Advanced textbooks in control and signal processing. Springer, 2000.
T. Sebastian. Temperature effects on torque production and efficiency of pm motors using ndfeb magnets. IEEE Transactions on Industry Applications, 31(2):
353–357, October 1995.
C. J. Seeton. Viscosity-temperature correlation for liquids. Tribology Letters, 22
(1):67–78, March 2006.
W. Seifert and V. Westcott. A method for the study of wear particles in lubricating
oil. Wear, 21(1):27 – 42, 1972.
SKF. Interactive engineering catalogue, August 2011. URL http://www.skf.
com/portal/skf/home/products?newlink=first&lang=en.
M. Spong, S. Hutchinson, and M. Vidyasagar. Robot modeling and control. John
Wiley & Sons, 2006.
M. W. Spong. Modeling and control of elastic joint robots. Journal of Dynamic
Systems, Measurement, and Control, 109(4):310–318, 1987.
R. Stribeck. Die wesentlichen eigenschaften der gleit- und rollenlager – The key
qualities of sliding and roller bearings. Zeitschrift Des Vereins Deutscher Ingenieure, 46(38–39):1342–1437, 1902.
W. Susanto, R. Babuska, F. Liefhebber, and T. van der Weiden. Adaptive friction
compensation: application to a robotic manipulator. In Proceedings of 17th
IFAC World Congress, Seoul, South Korea, December 2008.
J. Swevers, C. Ganseman, D. Tukel, J. de Schutter, and H. Van Brussel. Optimal
robot excitation and identification. IEEE Transactions on Robotics and Automation, 13(5):730 – 740, October 1997.
J. I. Taylor. The Vibration Analysis Handbook. Vibration Consultants, February
1994.
Bibliography
71
D. Tencer. Global robot population grew 12% in 2013, and that’s just the beginning, June 2013. URL http://www.huffingtonpost.ca/2014/06/12/
global-robot-population_n_5489454.html.
M. Tenerz. Parameter Estimation in a Permanent Magnet Synchronous Motor.
Msc. thesis LiTH-ISY-EX-11/4495-SE, Department of Electrical Engineering,
Linköping University, 2011.
G. Thompson. Improving maintainability and reliability through design. Wiley,
1st edition, January 1999.
A. Tustin. The effects of backlash and of speed-dependent friction on the stability of closed-cycle control systems. Journal of the Institution of Electrical
Engineers - Part IIA: Automatic Regulators and Servo Mechanisms, 94(1):143–
151, May 1947.
H. L. Van Trees. Detection, Estimation and Modulation Theory, Part I. Wiley,
New York, 1st edition, 2001.
A. T. Vemuri and M. M. Polycarpou. A methodology for fault diagnosis in robotic
systems using neural networks. Robotica, 22(04):419–438, 2004.
R. Waiboer, R. Aarts, and B. Jonker. Velocity dependence of joint friction in
robotic manipulators with gear transmissions. In Proceedings of the 2005
ECCOMAS Thematic Conference Multibody Dynamics, pages 1–19, Madrid,
Spain, 2005.
E. Wernholt. Multivariable Frequency-Domain Identification of Industrial
Robots. Linköping studies in science and technology. Dissertations. no. 1138,
Linköping Studies in Science and Technology, SE-581 83 Linköping, Sweden,
November 2007.
E. Wernholt and S. Moberg. Nonlinear gray-box identification using local models
applied to industrial robots. Automatica, 47(4):650 – 660, 2011.
L. Westerlund. The extended arm of man – a history of the industrial robot.
Technical report, Informationsförlaget, Stockholm, Sweden, 2000.
M. Woydt and R. Wäsche. The history of the Stribeck curve and ball bearing
steels: The role of Adolf Martens. Wear, 268(11–12):1542–1546, 2010.
J. Wu, J. Wang, and Z. You. An overview of dynamic parameter identification
of robots. Robotics and Computer-Integrated Manufacturing, 26(5):414 – 419,
2010.
D.-Y. Yeung and C. Chow. Parzen-window network intrusion detectors. In Proceedings of the 16th International Conference on Pattern Recognition, volume 4, pages 385–388, Quebec, Canada, 2002. IEEE.
S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the bench-
72
Bibliography
mark Tennessee Eastman process. Journal of Process Control, 22(9):1567–1581,
2012.
O. Zeitouni, J. Ziv, and N. Merhav. When is the generalized likelihood ratio test
optimal? IEEE Transactions on Information Theory, 38(5):1597–1602, 1992.
Part II
Publications
Paper A
Friction in a Robot Joint – Modeling
and Identification of Load and
Temperature Effects
Authors:
André Carvalho Bittencourt and Svante Gunnarsson.
Edited version of the paper
A. C. Bittencourt and S. Gunnarsson. Static friction in a robot joint –
modeling and identification of load and temperature effects. Journal of
Dynamic Systems, Measurement, and Control, 134(5), July 2012.
Parts of this paper were previously published in:
A. C. Bittencourt, E. Wernholt, S. Sander-Tavallaey, and T. Brogårdh.
An extended friction model to capture load and temperature effects in
robot joints. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 6161–6167, Taipei,
Taiwan, October 2010.
Preliminary version:
Technical Report LiTH-ISY-R-3038, Dept. of Electrical Engineering, Linköping
University, SE-581 83 Linköping, Sweden.
Friction in a Robot Joint – Modeling and
Identification of Load and Temperature
Effects
André Carvalho Bittencourt and Svante Gunnarsson
Dept. of Electrical Engineering,
Linköping University,
SE–581 83 Linköping, Sweden
{andrecb,svante}@isy.liu.se
Abstract
Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Friction
models in robotics are typically considered to be dependent only on
joint speed. However, it is known that friction can be affected by other
factors.
In this paper, the typical friction phenomena and models used in
robotics are reviewed. It is shown how such models can be represented as a sum of functions of relevant states which are linear and
nonlinear in the parameters. The identification method described
in Golub and Pereyra (1973) is suggested for parameter identification
when all states are available. The discussion follows with a detailed
experimental study of friction in a robot joint under changes of joint
angle, load torque and temperature. Justified by their significance,
load torque and temperature are included in an extended friction
model. The proposed model is validated in a wide operating range,
considerably improving the prediction performance compared to a
standard model.
1 Introduction
Friction exists in all mechanisms to some extent. It can be defined as the tangential reaction force between two surfaces in contact. It is a nonlinear phenomenon
which is physically dependent on contact geometry, topology, properties of the
materials, relative velocity, lubricant, etc. (Al-Bender and Swevers, 2008). Friction has been constantly investigated by researchers due to its importance in several fields, see e.g. Dowson (1998). In this paper, friction has been studied in
industrial robot joints based on experiments.
77
Paper A
78
Friction in a Robot Joint
In robotics, one reason for the interest in friction is the need to model friction for
control purposes, see e.g. Kim et al. (2009); Guo et al. (2008); Olsson et al. (1998);
Bona and Indri (2005); Susanto et al. (2008). A precise friction model can considerably improve the overall performance of a manipulator with respect to accuracy
and control stability. Since friction can relate to the wear processes of mechanical systems (Blau, 2009), including robot joints (Bittencourt et al., 2011), there is
also interest in friction modeling for robot condition monitoring and fault detection, see e.g. Freyermuth (1991); Vemuri and Polycarpou (2004); McIntyre et al.
(2005); Mattone and Luca (2009); Brambilla et al. (2008); Caccavale et al. (2009);
Namvar and Aghili (2010); Bittencourt et al. (2011).
A friction model consistent with real experiments is necessary for successful simulation, design and evaluation. Due to the complexity of friction, it is often
difficult to obtain models that can describe all the empirical observations. See
Al-Bender and Swevers (2008) for a comprehensive discussion on friction physics
and first principle friction modeling. In a robot joint, the complex interactions of
components such as gears, bearings and shafts which are rotating/sliding at different velocities, makes physical modeling difficult. An example of an approach to
model friction of complex transmissions can be found in Waiboer (2007), where
the author designs joint friction models based on physical models of elementary
joint components such as helical gear pairs and pre-stressed roller bearings.
Empirically motivated friction models have been successfully used in many applications, including robotics, see e.g. Armstrong-Hélouvry (1991); Olsson et al.
(1998); Åström and Canudas-de Wit (2008); Harnoy et al. (2008). This category
of models was developed through time according to empirical observations of
the phenomenon. Considering a set of states x, and parameters θ, these model
structures M, can be described as,
f (x, θ) =
M
X
φ j (x, θ),
(M)
j=1
where f is the generalized friction and φ j are general nonlinear functions or regressors. This static nonlinear relation can be extended further to include dynamics by considering additional differential equations
ẋ =
Md
X
φ dj (x, θ).
j=1
q]T ,
The choice x = [z, q̇,
where q is a generalized coordinate and z is an internal
state vector related to the dynamic behavior of friction, with dynamics described
by a first-order differential equation, gives the set of Generalized Empirical Friction Model structures (gefm), see Al-Bender and Swevers (2008).
Among the gefm model structures, the LuGre model (Olsson et al., 1998) is a
common choice in the robotics community. For a revolute joint, the LuGre model
1 Introduction
79
structure ML , can be described as
τ f = σ0 z + σ1 ż + h(ϕ̇)
|ϕ̇|
z,
ż = ϕ̇ − σ0
g(ϕ̇)
(ML )
where τ f is the friction torque, ϕ is the joint motor angle. The state z is related
to the dynamic behavior of asperities in the interacting surfaces and can be interpreted as their average deflection, with stiffness σ0 and damping σ1 .
The function h(ϕ̇) represents the velocity strengthening (viscous) friction and
is dependent on the stress versus strain rate relationship of the lubricant. For
Newtonian fluids, the shear stress follows a linear dependency to the shear rate,
τ = µ ∂u
, where τ is the shear stress, ∂u
is the local shear rate and µ is the viscosity.
∂y
∂y
It is typical to consider a Newtonian behavior, yielding the relationship
h(ϕ̇) = f v ϕ̇
(1)
for the viscous behavior of friction.
The function g(ϕ̇) captures the velocity weakening of friction. Motivated by the
observations mainly attributed to Stribeck (Jacobson, 2003; Woydt and Wäsche,
2010; Bo and Pavelescu, 1982), g(ϕ̇) is commonly modeled as
g(ϕ̇) = f c + f s e
α
ϕ̇
− ϕ̇ s
,
(2)
where f c is the Coulomb friction, f s is called the standstill friction parameteri , ϕ̇s
is the Stribeck velocity and α is the exponent of the Stribeck nonlinearity. The
model structure ML with the h− and g functions given by (1) and (2) is a gefm
with
h
iT
h
iT
x = z, ϕ̇ , θ = σ0 , σ1 , f c , f s , f v , ϕ̇s , α .
According to Åström and Canudas-de Wit (2008), the LuGre model can successfully describe many of the friction characteristics.
Since z is not measurable, a difficulty with ML is the estimation of the dynamic
parameters [σ0 , σ1 ]. In Olsson et al. (1998), these parameters are estimated in a
robot joint by means of open loop experiments. Open-loop experiments are not
always possible, and it is common to accept only a static description of ML . In
steady-state, ML is equivalent to the steady-state model MS :
τ f (ϕ̇) = g(ϕ̇)sign(ϕ̇) + h(ϕ̇)
(MS )
which is fully described by the g- and h functions. In fact, ML adds dynamics to
MS . The typical choice for g− and h, as defined in (2) and (1), yields the static
i The parameter f is commonly denoted static friction, describing the friction value close to zero
s
speed. An alternative nomenclature was adopted to make a distinction between the dynamic (differential) and static (algebraic) description of friction.
Paper A
80
model structure M0 :
f
"
τ (ϕ̇) = f c + f s e
α #
ϕ̇
− ϕ̇ s
Friction in a Robot Joint
sign(ϕ̇) + f v ϕ̇.
(M0 )
This model structure is commonly used and was described in Bo and Pavelescu
(1982). For the fixed α = 1, M0 simplifies to the Tustin model, first introduced in
Tustin (1947). The model M0 requires a total of four parameters to describe the
velocity weakening regime, g(ϕ̇), and one parameter to capture viscous friction,
h(ϕ̇). See Section 3 for a description of friction velocity regimes and an interpretation of the parameters.
From empirical observations, it is known that friction can be affected by several
factors, e.g.:
• temperature,
• force/torque levels,
• position,
• velocity,
• acceleration,
• lubricant properties.
A shortcoming with the LuGre model structure, as with any gefm, is the dependence only of the states x = [z, q̇, q]. In more demanding applications, the effects
of the remaining variables can not be neglected. In Waiboer et al. (2005), the
author observes a strong temperature dependence, while in Olsson et al. (1998),
joint load torque and temperature are considered as disturbances and estimated
in an adaptive framework. In Gogoussis and Donath (1988); Dohring et al. (1993),
the effects of load are modeled as a linear effect on f c , in a model structure similar
to M0 . In the recent contribution of Hamon et al. (2010) the effects of load are revisited to include also a linear dependency on f s . However, more work is needed
in order to understand the influence of the different factors to the friction properties. A more comprehensive friction model is needed to improve tasks related
to design, control, simulation and evaluation in industrial robotics.
The objective of this paper is to analyze and model the effects of speed, joint
angle, load torques and temperature to friction in a robot joint. Only the steadystate behavior of friction is studied and possible dynamic effects of friction are
not considered. The phenomena are studied in joint 2 of an abb irb 6620 industrial robot, see Figure 1a. Two load torque components are examined, the torque
aligned to the joint degree of freedom (dof) and the resulting torque perpendicular to the joint’s dof. These torques are in the paper named manipulation load
torque τ ℓ and perpendicular load torque τ p , see Figure 1b.
By means of experiments, these variables are analyzed and an empirically motivated model is proposed. The task of modeling is to find a suitable static model
structure according to:
τ f (x ∗ , θ) =
M
X
j=1
h
φ j (x ∗ , θ)
i
x ∗ = ϕ̇, ϕ a , τ p , τ ℓ , ξ ,
(M∗ )
1 Introduction
81
τℓ
ϕ̇ a
ξ
τp
(a) abb irb 6620 robot with
150 kg payload and 2.2 m reach.
(b) Schematics of the three first joints including the torque definitions for joint 2.
Figure 1: The experiments were performed for joint 2 of an abb robot irb
6620. The variable ϕ a is the joint angle, ξ is the joint lubricant temperature,
τ ℓ is the manipulation load torque and τ p is the perpendicular load torque.
where ξ is the joint (more precisely, lubricanti ) temperature and ϕ a is the joint
angle at the arm side, i.e. after the gearbox.
Ideally, the chosen model should be coherent with the empirical observations and,
simultaneously, with the lowest dimension of θ, the parameter vector, and with
the lowest number of describing functions, i.e. small M. For practical purposes,
the choice of φ j ( · ) should also be suitable for a simple identification procedure.
The work presented here is based on Bittencourt et al. (2010), where a friction
model was proposed to describe the effects of load and temperature in a robot
joint. More detailed analysis of the modeling assumptions are presented, together
with a more general framework for identification of friction models. The paper
is organized as follows. Section 2 presents an identification method for general
friction models when all states are available. Section 3 reviews basic friction
phenomena and models for robot joints and presents an experiment to retrieve
friction data from a robot joint. In Section 4, a velocity-dependent friction model
is extended based on empirical observations to propose and validate a model
structure for M∗ . Conclusions and future work are discussed in Section 5.
i In the studies, the robot gearbox was lubricated with oil, not grease, which gave an opportunity
to obtain well defined temperature readings by having a temperature sensor in the circulating oil.
Paper A
82
Friction in a Robot Joint
2 Identification of Friction Models
f
For a given value of the friction states x k , the associated friction value τk can be
predicted by a static model description M. Denoting b
τ f (k|θ) the predicted value
f
with model parameters θ, the error ǫ(k, θ) , τk − b
τ f (k|θ) measures deviations
between the model and observations. A common criterion for the choice of θ is
the value giving the least sum of squared errors. For N observations, the least
squares estimate is given by
N
1 X1 2
b
θ N = arg min VN (θ) = arg min
ǫ (k, θ).
N
2
θ
θ
(3)
k=1
The minimum of (3) occurs where the gradient of the cost function VN (θ) is zero,
i.e.
N
1 X
∂
ψ(kθ)ǫ(k, θ) = 0,
VN (θ) = −
∂θ
N
k=1
where ψ(k, θ) ,
solved.
− ∂ ε(k, θ),
∂θ
and a nonlinear system of equations needs to be
In many cases, friction models will contain subsets of parameters that appear linearly in the functions φ j ( · ). Denoting this subset of parameters η, an alternative
description of a friction model is
f
τ (x, θ) =
Nη
X
φ j (x, ρ)ηj = φ(x, ρ)T η
(4)
j=1
h
i
where the parameter vector θ T = η T , ρ T has dimension (Nη + Nρ ) and is divided according to the manner they appear in the model, respectively linearly or
nonlinearly. For the model in (4), the gradient ψ(k, θ) takes the form
T T
∂
T
ψ(k, θ) = φ(x k , ρ)T ,
,
(5)
φ(x k , ρ) η
∂ρ
where it is easy to realize the separable nature of the model. As presented in
Golub and Pereyra (1973), the separable structure of the model can be explored
for identification. Defining the matrix [Φ(ρ)]k,j = φ j (x k , ρ)ηj , for any given ρ, the
solution for η is given by the ordinary least squares estimate
−1
h
i
f T
b
η = Φ† (ρ)τ f , Φ† (ρ) , ΦT (ρ)Φ(ρ)
ΦT (ρ), τ f , τ1f , · · · , τN
(6)
where Φ† (ρ) is the Moore-Penrose pseudoinverse of Φ(ρ) and τ f is the vector
of observations. Substituting this back in (3), the problem can be rewritten as
a function only of ρ, thus reducing the amount of parameters in the nonlinear
2 Identification of Friction Models
83
minimization. The resulting nonlinear problem is given as
⊥
f 2
b
ρ = arg min ||τ f − Φ(ρ)b
η ||2 = arg min ||PΦ(
ρ) τ || ,
ρ
ρ
⊥
†
PΦ(
ρ ) , I − Φ(ρ)Φ (ρ) (7)
⊥
where PΦ(
ρ ) is the projector on the orthogonal complement of the column space
of Φ(ρ). The idea for an identification procedure is thus to first find b
ρ from (7),
and then plug it back in (6) to find b
η . This illustrates the algorithm proposed in
bT
hGolub and
i Pereyra (1973), where it is also shown that the resulting point θ =
b
ηT , b
ρ T is a minimum of (3).
The nonlinear minimization in (7) can be solved using gradient based methods.
⊥
f
In Golub and Pereyra (1973), it is shown that the gradient of PΦ(
ρ ) τ requires
only computation of derivatives of Φ(ρ), as in (5), see Golub and Pereyra (1973)
for a detailed treatment. In this work, (7) is solved using a trust-region reflective
algorithm available in the Matlab’s Optmization Toolbox with initial estimates
given by a coarse grid search. The resulting estimate b
ρ is plugged in (6) to find b
η.
2.1 Covariance estimate
As presented in Ljung (1999), in case there is a parameter value θ0 such that
ǫ(k, θ 0 ) is a sequence of independent zero mean random variables with variance
γ 0 , then as the number of data points N tends to infinity, the least squares estimate b
θ N will converge in distribution as
√
N (b
θ N − θ0 ) ∈ As N (0, Pθ )
(8)
where Pθ is given by


N


1 X
0
0 T
Eψ(k, θ )ψ(k, θ ) 
Pθ = γ  lim
N →∞ N
0

(9)
k=1
and the asymptotic covariance for b
θ N is thus Σθ = N1 Pθ . The finite sample
estimate of Σθ is given by
N
−1
X

T

b
bN 
Σb = γ
ψ(k, b
θ N )ψ(k, b
θ N ) 
(10)
θN
bN =
γ
1
N
k=1
N
X
k=1
ǫ2 (k, b
θ N ).
(11)
The quantity in (10) is used throughout this work as a covariance estimate for b
θN
and provides a measure of accuracy for the resulting estimates.
Paper A
84
Friction in a Robot Joint
3 Basics of Friction Phenomena in a Robot Joint
Friction is typically presented in a friction curve, a plot of friction levels against
speed achieved in stationary conditions. The friction curve is related to the
Stribeck curve (Woydt and Wäsche, 2010) under the simplification that viscosity and load are constant. An example of a friction curve for a robot joint can be
seen in Figure 3.
From a phenomenological perspective, a friction curve can be divided into three
regimes, according to the lubrication characteristics: boundary (bl), mixed (ml)
and elasto-hydrodynamic lubrication (ehl). The phenomena present in very low
velocities (bl) is mostly related to interactions between the asperities of the surfaces in contact. With the increase of velocity, there is a consequent increase of
the lubrication film between the surfaces and a decrease of friction (ml) until it
reaches a full lubrication profile (ehl) with a separation of the surfaces by the lubricant. In ehl, friction is proportional to the force needed to shear the lubricant
layer, thus dependent on the lubricant properties, in particular its viscosity. Recalling the steady-state friction model MS , the bl and ml regimes are described
by the velocity weakening function g− and the ehl regime is described by h.
In the next subsection, an experimental procedure is suggested to retrieve the
friction curve of a robot joint based on constant-speed experiments. Using the
identification method described in Section 2, different models to describe the
observed speed dependency are found and some model simplifications are motivated.
3.1 A procedure to estimate friction at a fixed speed level
A manipulator is a multivariable, nonlinear system that can be described in a
general manner through the rigid multi-body dynamic model
M(ϕ)ϕ̈ + C(ϕ, ϕ̇)ϕ̇ + τ g (ϕ) + τ f (ϕ̇) = τ,
(12)
where M(ϕ) is the inertia matrix, C(ϕ, ϕ̇) relates to speed dependent terms (Coriolis and centrifugal), τ g (ϕ) are the gravity-induced torques and τ f contains the
friction torques. The system is controlled by the input torque, τ, applied by the
joint motor (in the experiments the torque reference from the servo was measuredi ).
For single joint movements (so that centrifugal forces are zero at that joint) and
under constant speed (so that inertial torques are zero), the applied torque at the
joint under actuation simplifies to
τ g (ϕ) + τ f (ϕ̇) = τ
(13)
and drives only friction and gravity-induced torques. The required torques to
drive a joint in forward, τ + , and reverse, τ − , directions at the constant speed
i It is known that using the torque reference from the servo as a measure of the joint torque might
not always hold because of the temperature dependence of the torque constant of the motors. The
deviations are however considered to be small and are neglected during the experiments.
3 Basics of Friction Phenomena in a Robot Joint
85
50
1
ϕ
ϕ̇
ϕ̄
τ
0.5
0
0
−50
−100
0
τ
ϕ [rad], ϕ̇ [rad/s]
100
−0.5
2
4
t [sec]
6
8
−1
Figure 2: Excitation signals used for the friction estimation at ϕ̇= 42 rad/s
and ϕ = 0 rad.
level ϕ̇, and at a joint angle value ϕ (so that τ g (ϕ) is equal in both directions), are
τ f (ϕ̇) + τ g (ϕ) = τ +
τ f (−ϕ̇) + τ g (ϕ) = τ − .
(14)
τ g (ϕ)
In the case an estimate of
is available, it is possible to isolate the friction
component in each direction using Equation (14). If such estimate is not possible
(e.g. not all masses are completely known), τ f can still be retrieved in the case
that τ f is independent of the rotation direction. Subtracting the equations yields
τ f (ϕ̇) − τ f (−ϕ̇) = τ + − τ −
and if τ f (−ϕ̇) = −τ f (ϕ̇), the resulting direction independent friction is:
τ+ − τ−
.
(15)
2
In the experiments, each joint is moved separately with the desired speed in both
directions around a given joint angle ϕ. Figure 2 shows the measured joint angle-,
speed- and torquei signals for ϕ̇= 42 rad/s around ϕ= 0 rad. The constant speed
data is segmented around ϕ and the friction levels can be achieved using Equation (14) or (15).
τ f (ϕ̇) =
The procedure can be repeated for several different speeds and a friction curve
can be drawn, as displayed by the circles in Figure 3. As shown in Figure 3,
there is only a small direction dependency of friction for the investigated joint.
Therefore, in this paper, friction levels are achieved using Equation (15), which
is not influenced by deviations in the gravity model of the robot.
i Throughout the paper all torques are normalized to the maximum manipulation torque at low
speed and are therefore presented as dimensionless quantities.
Paper A
86
Friction in a Robot Joint
Table 1: Identified M0 parameters for the data shown in Figure 3.
f c [ 10−2 ]
f s [ 10−2 ]
f v [ 10−4 ]
ϕ̇s
α
3.4 ± 0.176 4.6 ± 0.48 3.68 ± 0.12 10.68 ± 1.08 1.93 ± 0.60
0.14
τf
0.12
0.1
fµ
fv
0.08
f s 0.06
fc
BL
0
ϕ̇s
50
100
ML
EHL
150
ϕ̇ [rad/s]
τf
200
M0
M0µ
250
Figure 3: Friction curve with lubrication regimes and model-based predictions. Squares indicate friction levels achieved using Equation (15), the (almost indistinguishable) superimposed circles are friction levels achieved using Equation (14).
3.2 Modeling of velocity dependencies
Model structure M0 is a common choice to describe the friction dependency with
speed. For a direction independent friction, it suffices to describe friction for
positive velocities, which is a convention adopted from here and on. For positive
velocities, M0 can be written as in Equation (4) with
α
ϕ̇
x = ϕ̇,
φ(ϕ̇, ρ) = 1, e− ϕ̇s , ϕ̇
h
iT
h
iT
η = fc , fs , fv ,
ρ = ϕ̇s , α .
The model parameters are identified using the direction independent data (circles) in Figure 3. The resulting identified parameters values are shown in Table 1
with one standard deviation. The dark solid line in Figure 3 is obtained by modelbased predictions of the resulting model, with sum of absolute errors smaller
than 3.0 10−2 .
A closer investigation of the friction curve in Figure 3 reveals that the behavior of friction at high speeds is slightly nonlinear with speed. This feature is
related to the non-Newtonian behavior of the lubricant at high speeds, see e.g.
Waiboer et al. (2005). In this case, the fluid presents a pseudoplastic behavior,
with a decrease of the apparent viscosity with shear rate. The behavior motivates
0
40
30
20
10
50 100 150 200 250
ϕ̇ [rad/s]
1
α
1
0.8
0.6
0.4
0.2
0.5
0
0
0.5
0
0
e −|ϕ̇/ ϕ̇s |
e −|ϕ̇/ ϕ̇s |
α
2.5
2
1.5
1
40
30
20
10
50 100 150 200 250
ϕ̇ [rad/s]
(b) α = 1.5, ϕ̇s ∈ (1, 50) rad/s.
α
(a) α = 0.5, ϕ̇s ∈ (1, 50) rad/s.
1
87
e −|ϕ̇/ ϕ̇s |
e −|ϕ̇/ ϕ̇s |
α
3 Basics of Friction Phenomena in a Robot Joint
1
0.8
0.6
0.4
0.2
0
50 100 150 200 250
ϕ̇ [rad/s]
(c) ϕ̇s = 25 rad/s, α ∈ (0.02, 3.00).
2.5
2
1.5
1
50 100 150 200 250
ϕ̇ [rad/s]
(d) ϕ̇s = 100 rad/s, α ∈ (0.02, 3.00).
Figure 4: Effects of changes of ϕ̇s and α to the friction curve.
the suggestion of an alternative model structure
f
τ (ϕ̇) = f c + f s e
α
ϕ̇
− ϕ̇ s
+ f v ϕ̇ + f µ ϕ̇ β ,
(M0µ )
where f µ and β relate to the non-Newtonian part of the viscous friction behavior
and capture the deviation from a Newtonian behavior. The parameters for this
model are identified for the friction curve in Figure 3. The resulting predictions
are shown by the gray solid line in Figure 3, with sum of absolute prediction error
of 5.5 10−3 .
Despite the non-Newtonian behavior of the lubricant, the increase in accuracy
achieved with M0µ is relatively small compared to M0 . As it will be shown, other
effects are considerably more significant. For simplicity of the resulting model,
M0 is considered as a basis to describe the dependencies with speed and the nonNewtonian behavior is not considered further in this paper.
Fixing α
The model M0 describes
the velocity weakening regime, g( · ), through the expo
−
ϕ̇ α
nential term e ϕ̇s and takes two nonlinear parameters α and ϕ̇s . To further
simplify the description, it is common to accept α as a constant between 0.5 and
2 (Åström and Canudas-de Wit, 2008; Olsson et al., 1998; Susanto et al., 2008).
As seen in Figure 4, ϕ̇s changes the constant of the decay while α changes its
curvature. Notice from Figures 4a and 4b that small choices of α can considerably affect friction at high speeds. This is not desirable since the high speed
effects should be described by the velocity strengthening function h( · ). For these
reasons, α is fixed as presented next.
Paper A
100
kǫ (α)k22 −kǫ (α ∗ )k22
kǫ (α ∗ )k22
88
160
140
120
100
80
60
40
20
0
0
1
1.36
α
2
Friction in a Robot Joint
3
Figure 5: Relative cost increase as a function of α for M0 .
Considering all friction data presented in this work, in a total of 488 friction
curves with more than 5800 samples, α is chosen as the value minimizing Equation (3) for the model structure M0 when all other parameters are free at each
friction curve. Figure 5 presents the resulting relative increase in the cost for
different values of α. The value with minimal cost gives α ∗ = 1.36 ± 0.011.
4 Empirically Motivated Modeling
Using the friction estimation method described in Section 3.1, it is possible to
design a set of experiments to analyze how the states x ∗ affect friction. As shown
in Section 3.2, the model structure M0 can represent the friction dependence on
speed, ϕ̇, fairly well. M0 is therefore taken as a primary choice, with α fixed at
α ∗ = 1.36. Whenever a single instance of M0 can not describe the observed friction behavior, extra terms φ j (x ∗ , θ) are proposed and included in M0 to achieve
a satisfactory model structure M∗ .
4.1 Guidelines for the experiments
It is important to isolate the influences of the different variables considered when
modeling. The situation is particularly critical regarding temperature as it is
difficult to control inside a robot joint. Moreover, due to the complex structure
of an industrial robot, changes in joint angle might move the mass center of the
robot arm system, causing variations of the load torques. To avoid undesired
effects, the guidelines below were followed during the experiments.
Isolating joint load torque dependency from joint angle dependency
Using an accurate robot modeli , it is possible to predict the load torques at the
joints for a given robot configuration (a set of all joints’ angles). For example,
Figure 6 shows the resulting τ ℓ and τ p at joint two for configurations depending
on the achievable angles for joints 2 and 4 (ϕ2a and ϕ4a ). Using this information, a
i An abb internal tool was used for simulation purposes.
89
0.5
0.06
0
0.04
τp
τℓ
4 Empirically Motivated Modeling
−0.5
0.02
−1
0
ϕ2a [deg] 100 200
500
0 a
ϕ4 [deg]
(a) Simulated τ ℓ .
0
0
ϕ2a [deg] 100 200
500
0a
ϕ4 [deg]
(b) Simulated τ p .
Figure 6: Simulated load torques at joint two caused by angle variations of
joints 2 and 4, ϕ2a and ϕ4a respectively. Notice the larger achievable range of
values for τ ℓ compared to τ p .
set of configurations can be selected a priori in which it is possible to isolate these
effects.
Isolating temperature effects
Using joint lubricant temperature measurements, the joint thermal decay constant κ was estimated to 3.04 h. By executing the friction curve estimation experiment periodically, for longer time than 2κ (i.e. > 6.08 h), the joint temperature is
expected to have reached an equilibrium. Only data collected under an expected
thermal equilibrium was considered for the analysis.
4.2 Effects of joint angles
Due to asymmetries in the contact surfaces, it has been observed that friction in
rotating machines can depend on the angular position (Al-Bender and Swevers,
2008). It is therefore expected that this dependency occurs also in a robot joint.
Following the experiment guidelines from the previous section, a total of 50
friction curves were estimated with variations of the joint angle in the range
ϕ a ∈ (8.40, 59.00) deg. As seen in Figure 7a, only small effects can be observed.
The subtle deviations are hcomparable toi the errors of the friction curve identified
under constant values of ϕ a , τ p , τ ℓ , ξ . In fact, even a constant instance of M0
can describe the friction curves satisfactorily, no extra terms are thus required.
4.3 Effects of load torques
Since friction is related to the interaction between contacting surfaces, one of the
first phenomenon observed was that friction varies according to the applied normal force. This can be explained by the increase of the true contact area between
the surfaces under large normal forces. A similar reasoning can be extended to
load torques in a revolute robot joint. Due to the elaborated gear- and bearing de-
Paper A
90
0.14
30
25
35
40
0.1
0.14
0.05 0.06 0.07 0.08 0.09 0.1
0.12
τf
τf
0.12
20
0.1
0.08
0.08
0.06
0.06
0
50 100 150 200 250
ϕ̇ [rad/s]
(a) Effects of ϕ a at τ ℓ = −0.39, ξ = 34◦ C.
Friction in a Robot Joint
0
50 100 150 200 250
ϕ̇ [rad/s]
(b) Effects of τ p at τ ℓ = −0.39, ξ = 36◦ C.
Figure 7: Friction curves for experiments related to ϕ a and τ p .
sign of the joint, it is also expected that torques in different directions will have
different effects to the friction curvei .
Only small variations of τ p , the perpendicular load torque, are achievable because of the mechanical construction of the robot, see Figure 6b. A total of 20 friction curves achieved at constant temperature were retrieved for joint two, where
τ p was varied in the range τ p ∈ (0.04, 0.10). As Figure 7b shows, the influences
of τ p for the achievable range did not cause large changes to the friction curves.
The model M0 is thus considered sufficient.
Large variations of τ ℓ , the manipulation load torque, are possible by simply varying the arm configuration as seen in Figure 6a. A total of 50 friction curves were
estimated where τ ℓ was varied over the range τ ℓ ∈ (−0.73, 0.44). As seen in Figure 8, the effects are significant.
Clearly, a single instance of M0 can not describe the observed phenomena. A
careful analysis of the effects reveals that the main changes occur in the velocity
weakening part of the curve. From Figure 8c, it is possible to observe a linear
bias-like (f c ) increase and a linear increase of the standstill friction (f s ) with |τ ℓ |.
Furthermore, as seen in Figure 8b, the Stribeck velocity ϕ̇s is maintained fairly
constant. The observations support an extension of M0 to
f
ℓ
ℓ
ℓ
τ (ϕ̇, τ ) = {f c,0 + f c,ℓ |τ |} + {f s,0 + f s,ℓ |τ |}e
∗
ϕ̇ α
− ϕ̇ s,ℓ
+ f v ϕ̇.
(Mℓ )
The model structure Mℓ is similar to the one presented in Hamon et al. (2010),
where the changes in f c and f s appear as linear functions of |τ ℓ |.
4.4 Effects of temperature
In lubricated mechanisms, the friction properties are related to both the thickness of the lubricant layer and its viscosity which, in turn, can vary with temperi In fact, a full joint load description would require three torque and three force components.
4 Empirically Motivated Modeling
91
0.2
τf
0.15
0.1
0.05
0.5
0
τℓ
−0.5
−1
100
0
200
ϕ̇ [rad/s]
300
(a) Friction levels as a function of ϕ̇ and τ ℓ .
0.16
0.4
0.6
0.8
1
0.14
0.12
0.12
0.1
0.1
τf
τf
0.14
0.16
0.2
0.08
0.08
0.06
0.06
0.04
0
50 100 150 200 250
ϕ̇ [rad/s]
(b) Friction levels as a function of ϕ̇ for
different values of τ ℓ .
0.04
50 100 150 200 250 300
−0.6 −0.4 −0.2 0
τℓ
0.2 0.4
(c) Friction levels as a function of τ ℓ for
different values of ϕ̇ .
Figure 8: The dependencies of friction with the manipulation load torque,
τ ℓ , at ξ = 34◦ C.
Paper A
92
Friction in a Robot Joint
ature (Seeton, 2006). Dedicated experiments were made to analyze the effects of
temperature. At first, the joint was warmed up to 81.2◦ C by running the joint
continuously back and forth. Then, while the robot cooled, 50 friction curves
were retrieved with variations of ξ over the range ξ ∈ (38.00, 81.20) ◦ C. In order to resolve combined effects of ξ and τ ℓ , two manipulation torque levels were
used, τ ℓ = −0.02, and τ ℓ = −0.72. As it can be seen in Figure 9, the effects of ξ are
significant.
Temperature has an influence on both velocity regions of the friction curves. In
the velocity-weakening region, a linear increase of the standstill friction (f s ) with
temperature can be observed according to Figure 9b. In Figure 9c, it can be seen
that the Stribeck velocity (ϕ̇s ) increases linearly with temperature. The effects
in the velocity-strengthening region appear as a nonlinear, exponential-like, decrease of the velocity-dependent slope (f v ), as seen in Figures 9b and 9c.
Combined effects of τ ℓ and ξ
It is also important to study possible combined effects of load and temperature.
To visualize possible co-effects, the friction surfaces in Figure 9a are subtracted
from each other, yielding e
τ f . As it can be seen in Figure 10a, the result is fairly
temperature independent. This can be interpreted as an indication that the variables are additively separable, i.e. they appear in separate additive terms. Under
this modeling assumption, it is possible to subtract the τ ℓ -effects from the surfaces in Figure 9a and solely obtain temperature related phenomena. The previously proposed terms to describe the τ ℓ -effects in Mℓ were
f
ℓ
ℓ
α ∗
ϕ̇ ℓ − ϕ̇s τ (τ ) = f c,ℓ |τ | + f s,ℓ |τ |e
.
(16)
The parameters for this model were found from an identification of model Mℓ
using the data set from Figure 8. With the achieved parameters, the computed
effects of the load τ f (τ ℓ ) were subtracted from the friction data of Figure 9a, i.e.,
the quantities τ f −b
τ f (τ ℓ ) were computed. The resulting friction levels are shown
in Figure 10b. As can be seen, the resulting friction levels become very similar,
independent of the manipulation load torques used. This further supports the
modeling assumption that the effects of temperature and load are additively separable. Even after removing the load effects, the original model structure cannot
fully characterize all observed phenomena and new terms should be added to
describe the temperature effects.
4 Empirically Motivated Modeling
93
τ ℓ = −0.02
τ ℓ = −0.72
0.2
τf
0.15
0.1
0.05
100
80
60
ξ [◦ C]
40
20
0
100
200
300
ϕ̇ [rad/s]
(a) Friction levels as a function of ϕ̇ and ξ for two different levels
of τ ℓ .
0.14
40
50
60
70
0.14
80
0.12
0.1
0.1
τf
τf
0.12
50 100 150 200 250 300
0.08
0.08
0.06
0.06
0.04
0
50 100 150 200 250
ϕ̇ [rad/s]
(b) Friction levels as a function of ϕ̇ for
different values of ξ at τ ℓ = −0.02.
0.04
40
ϕ̇s at intersection
50
80
60
70
ξ [◦ C]
(c) Friction levels as a function of ξ for
different values of ϕ̇ at τ ℓ = −0.02.
Figure 9: The dependencies of friction with temperature, ξ .
Paper A
94
Friction in a Robot Joint
0.08
0.06
e
τf
0.04
0.02
100
80
60
ξ [◦ C]
40
20
0
100
200
300
ϕ̇ [rad/s]
(a) Difference e
τ f between the friction data in Figure 9a.
τf − b
τ f (τ ℓ )
τ ℓ = −0.72
τ ℓ = −0.02
0.1
0.05
100
80
ξ [◦ C]
60
40
20
0
100
200
300
ϕ̇ [rad/s]
(b) Friction data of Figure 9a after subtraction of the τ ℓ dependent terms.
Figure 10: Indication that the effects of ξ and τ ℓ are additively separable.
4 Empirically Motivated Modeling
95
Table 2: Identified parameters for M∗ .
f c,0 [ 10−2 ]
f c,ℓ [ 10−2 ]
f s,ℓ [ 10−1 ]
ϕ̇s,ℓ
3.11 ± 0.028 2.34 ± 0.071
1.26 ± 0.025
9.22 ± 0.12
f s,0 [ 10−2 ]
f s,ξ [ 10−3 ]
ϕ̇s,0
ϕ̇s,ξ
−2.50 ± 0.12 1.60 ± 0.022 −24.81 ± 0.87 0.98 ± 0.018
f v,0 [ 10−4 ]
f v,0 [ 10−4 ]
ξVo
α ∗ (fixed)
1.30 ± 0.056 1.30 ± 0.056
20.71 ± 0.91
1.36
4.5 A proposal for M∗
From the characteristics of the ξ-related effects and the already discussed τ ℓ effects, Mℓ is extended to:
τ f (ϕ̇, τ ℓ , ξ) = {f c,0 + f c,ℓ |τ ℓ |} + f s,ℓ |τ ℓ |e
+ {f s,0 + f s,ξ ξ}e
− {ϕ̇
−ξ
ϕ̇
s,0 +ϕ̇s,ξ
+ {f v,0 + f v,ξ e ξVo }ϕ̇.
∗
ϕ̇ α
− ϕ̇ s,ℓ
α ∗
ξ} (M∗g,ℓ )
(M∗g,ξ )
(M∗h,ξ )
In the above equation, the parameters are written with subscripts _0 , _ℓ or _ξ in
order to clarify its origin related to M0 , τ ℓ or ξ. The first M∗g expressions relate
to the velocity-weakening friction while M∗h relates to the velocity-strengthening
regime. The load τ ℓ only affects the velocity-weakening regime and requires a
total of three parameters, [f c,ℓ , f s,ℓ , ϕ̇s,ℓ ]. The temperature ξ affects both regimes
and requires four parameters, [f s,ξ , ϕ̇s,ξ , f v,ξ , ξVo ]. The four remaining parameters, [f c,0 , f s,0 , ϕ̇s,0 , f v,0 ] , relate to the original friction model structure M0 . Notice that under the modeling assumption that τ ℓ - and ξ effects are additively
separable, their respective expressions appear as separated sums in M∗ .
The term f v,ξ e −ξ/ξVo in M∗h,ξ is motivated by the exponential-like behavior of
viscous friction (recall Figure 9c). In fact, the parameter ξVo is a reference to
the Vogel-Fulcher-Tamman exponential description of viscosity and temperature.
This description is valid for the temperature range considered here and more
complex expressions may be needed for larger temperature variations, see e.g.
Seeton (2006).
4.6 Validation
The parameters for the proposed model are identified based on friction data from
Figures 8 and 9 and are presented in Table 2. A separate data set is used for the
validation of the proposed model structure M∗ . It consists of several friction
curves retrieved at different τ ℓ - and ξ values, as seen in Figure 11. The distribution of the prediction errors, p(ǫ), achieved with the validation data set is shown
in Figure 12. For a comparison, the distribution of the errors related to an instance of M0 with parameters given in Table 1 is also shown. As it can be seen,
Paper A
96
Friction in a Robot Joint
0.2
0.15
τf
0.1
0.05
300
200
100
k
0
100
0
300
200
ϕ̇ [rad/s]
(a) Friction data used for validation.
1
35
ξ[◦ C]
0.5
40
ξ
τℓ
30
−0.5
25
τℓ
0
−10
50
100
k
150
200
20
250
(b) τ ℓ - and ξ conditions used in each friction curve.
Figure 11: Validation data set. Notice the large variations of ξ - and τ ℓ in (b)
when registering the friction curves in (a).
100
p(ǫ)
80
M0
M∗
60
40
20
0
−0.06 −0.04 −0.02
0
0.02
0.04
0.06
τf
Figure 12: Distribution of the prediction errors for the models M0 and M∗
achieved using the validation data set. Notice the considerable improved
performance for M∗ .
5 Conclusions and Further Research
97
M∗ is able to capture considerably more of the friction behavior than M0 , with
only speed dependence. The mean, standard deviation and largest absolute error for M∗ are respectively −9.24 10−4 , 4.23 10−3 , 1.88 10−2 , compared to 1.09 10−2 ,
1.34 10−2 , 7.58 10−2 for M0 . The proposed model structure has also been successfully validated in other joints with similar gearboxes, but it might be interesting
to validate it in other robot types and even other types of rotating mechanisms.
5 Conclusions and Further Research
The main contribution of this paper is the empirical analysis of the effects of
position, speed, load and temperature to friction in a robot joint and the proposed
model to describe the most significant effects of speed, load and temperature. As
shown in the validation results, a model that includes a description of load and
temperature might be needed for a more accurate representation of friction.
In the studies, the friction phenomena were fairly direction independent. If this
was not the case, two instances of the proposed model could be used to describe
the whole speed range, but requiring two times more parameters. The proposed
model has a total of 11 parameters, four of those enter the model in a nonlinear fashion. The identification of such a model is computationally costly and
requires data from several different operating conditions. Studies on defining
sound identification excitation routines are therefore important.
Only steady-state friction (measured when transients caused by velocity changes
have disappeared) was considered in the studies. It would be interesting to investigate if a dynamic model, for instance given by the LuGre model structure,
could be used to describe dynamic friction with extensions from the proposed
model. This task presents practical experimental challenges and should perhaps
be performed in a robot joint mounted in a test bench instead of on a robot arm
system which has complex dynamics.
A practical limitation of the proposed model is the requirement on availability of
load torque and temperature estimates. Up to date, torque- and joint temperature
sensors are not available in standard industrial robots. As mentioned in Section
4.1, the joint torque components can be estimated from the torque reference to
the drive system by means of an accurate robot model. In this situation, it is important to have correct load parameters in the model to calculate the components
of the load torques.
Despite these experimental challenges, there is a great potential for the use of
the proposed model for simulation-, design- and evaluation purposes. The designer of control algorithms, the diagnosis engineer, the gearbox manufacturer,
etc. would benefit with the use of more realistic friction models.
98
Paper A
Friction in a Robot Joint
Bibliography
F. Al-Bender and J. Swevers. Characterization of friction force dynamics. IEEE
Control Systems Magazine, 28(6):64–81, 2008.
B. Armstrong-Hélouvry. Control of Machines with Friction. Springer, 1991.
K. J. Åström and C. Canudas-de Wit. Revisiting the LuGre friction model. IEEE
Control Systems Magazine, 28(6):101–114, December 2008.
A. C. Bittencourt, E. Wernholt, S. Sander-Tavallaey, and T. Brogårdh. An extended
friction model to capture load and temperature effects in robot joints. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots
and Systems, pages 6161–6167, Taipei, Taiwan, October 2010.
A. C. Bittencourt, P. Axelsson, Y. Jung, and T. Brogårdh. Modeling and identification of wear in a robot joint under temperature disturbances. In Proceedings
of the 18th IFAC World Congress, volume 18, Milan, Italy, August 2011.
P. J. Blau. Embedding wear models into friction models. Tribology Letters, 34(1),
April 2009.
L. C. Bo and D. Pavelescu. The friction-speed relation and its influence on the
critical velocity of stick-slip motion. Wear, 82(3):277–289, 1982.
B. Bona and M. Indri. Friction compensation in robotics: an overview. In Proceedings of the 44th IEEE International Conference on Decision and Control,
Seville, Spain, December 2005.
D. Brambilla, L. Capisani, A. Ferrara, and P. Pisu. Fault detection for robot manipulators via second-order sliding modes. IEEE Transactions on Industrial
Electronics, 55(11):3954–3963, November 2008.
F. Caccavale, P. Cilibrizzi, F. Pierri, and L. Villani. Actuators fault diagnosis for
robot manipulators with uncertain model. Control Engineering Practice, 17(1):
146–157, 2009.
M. Dohring, E. Lee, and W. Newman. A load-dependent transmission friction
model: theory and experiments. In Proceedings of the 1993 IEEE International
Conference on Robotics and Automation, pages 430–436, Atlanta, USA, May
1993.
D. Dowson. History of Tribology. Professional Engineering Publishing, London,
UK., 2nd edition, 1998.
B. Freyermuth. An approach to model based fault diagnosis of industrial robots.
In Proceedings of the 1991 IEEE International Conference on Robotics and Automation, volume 2, pages 1350–1356, April 1991.
A. Gogoussis and M. Donath. Coulomb friction effects on the dynamics of bearings and transmissions in precision robot mechanisms. In Proceedings of the
Bibliography
99
1998 IEEE International Conference on Robotics and Automation, pages 1440–
1446, Leuven, Belgium, April 1988.
G. H. Golub and V. Pereyra. The differentiation of pseudo-inverses and nonlinear
least squares problems whose variables separate. SIAM Journal on Numerical
Analysis, 10(2):413–432, April 1973.
Y. Guo, Z. Qu, Y. Braiman, Z. Zhang, and J. Barhen. Nanotribology and nanoscale
friction. IEEE Control Systems Magazine, 28(6):92–100, December 2008.
P. Hamon, M. Gautier, and P. Garrec. Dynamic identification of robots with a
dry friction model depending on load and velocity. In Proceedings of the 2010
IEEE/RSJ International Conference on Intelligent Robots and Systems, pages
6187–6193, Taipei, Taiwan, October 2010.
A. Harnoy, B. Friedland, and S. Cohn. Modeling and measuring friction effects.
IEEE Control Systems Magazine, 28(6), December 2008.
B. Jacobson. The Stribeck memorial lecture. Tribology International, 36(11):781–
789, 2003.
H. M. Kim, S. H. Park, and S. I. Han. Precise friction control for the nonlinear
friction system using the friction state observer and sliding mode control with
recurrent fuzzy neural networks. Mechatronics, 19(6):805–815, 2009.
L. Ljung. System Identification: Theory for the User. Prentice Hall PTR, 2nd
edition, December 1999.
R. Mattone and A. D. Luca. Relaxed fault detection and isolation: An application
to a nonlinear case study. Automatica, 42(1):109 – 116, 2009.
M. McIntyre, W. Dixon, D. Dawson, and I. Walker. Fault identification for robot
manipulators. IEEE Transactions on Robotics, 21(5):1028–1034, October 2005.
M. Namvar and F. Aghili. Failure detection and isolation in robotic manipulators
using joint torque sensors. Robotica, 28(4):549–561, 2010.
H. Olsson, K. J. Åström, C. C. de Wit, M. Gafvert, and P. Lischinsky. Friction
models and friction compensation. European Journal of Control, 4(3):176–195,
1998.
C. J. Seeton. Viscosity-temperature correlation for liquids. Tribology Letters, 22
(1):67–78, March 2006.
W. Susanto, R. Babuska, F. Liefhebber, and T. van der Weiden. Adaptive friction
compensation: application to a robotic manipulator. In Proceedings of 17th
IFAC World Congress, Seoul, South Korea, December 2008.
A. Tustin. The effects of backlash and of speed-dependent friction on the stability of closed-cycle control systems. Journal of the Institution of Electrical
Engineers - Part IIA: Automatic Regulators and Servo Mechanisms, 94(1):143–
151, May 1947.
100
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Friction in a Robot Joint
A. T. Vemuri and M. M. Polycarpou. A methodology for fault diagnosis in robotic
systems using neural networks. Robotica, 22(04):419–438, 2004.
R. Waiboer. Dynamic Modelling, Identification and Simulation of Industrial
Robots. PhD thesis, University of Twente, 2007.
R. Waiboer, R. Aarts, and B. Jonker. Velocity dependence of joint friction in
robotic manipulators with gear transmissions. In Proceedings of the 2005
ECCOMAS Thematic Conference Multibody Dynamics, pages 1–19, Madrid,
Spain, 2005.
M. Woydt and R. Wäsche. The history of the Stribeck curve and ball bearing
steels: The role of Adolf Martens. Wear, 268(11–12):1542–1546, 2010.
Paper B
Modeling and Experiment Design for
Identification of Wear in a Robot Joint
under Load and Temperature
Uncertainties based on Friction Data
Authors:
André Carvalho Bittencourt and Patrik Axelsson.
Edited version of the paper:
A. C. Bittencourt and P. Axelsson. Modeling and experiment design for
identification of wear in a robot joint under load and temperature uncertainties based on friction data. IEEE/ASME Transactions on Mechatronics, 19(5):1694–1706, October 2014.
Parts of this paper were previously published in:
A. C. Bittencourt, P. Axelsson, Y. Jung, and T. Brogårdh. Modeling and
identification of wear in a robot joint under temperature disturbances.
In Proceedings of the 18th IFAC World Congress, volume 18, Milan,
Italy, August 2011.
Modeling and Experiment Design for
Identification of Wear in a Robot Joint under
Load and Temperature Uncertainties based
on Friction Data
André Carvalho Bittencourt and Patrik Axelsson
Dept. of Electrical Engineering,
Linköping University,
SE–581 83 Linköping, Sweden
{andrecb,axelsson}@isy.liu.se
Abstract
The effects of wear to friction are studied based on constant-speed
friction data collected from dedicated experiments during accelerated
wear tests. It is shown how the effects of temperature and load uncertainties produce larger changes to friction than those caused by wear,
motivating the consideration of these effects. Based on empirical observations, an extended friction model is proposed to describe the effects of speed, load, temperature and wear. Assuming availability of
such model and constant-speed friction data, a maximum likelihood
wear estimator is proposed. The performance of the wear estimator
under load and temperature uncertainties is found by means of simulations and verified under three case studies based on real data. Practical issues related to experiment length are considered based on a criterion for optimal selection of speed points to collect friction data that
minimizes the mean square estimation error for any unbiased wear estimator. As it is shown, reliable wear estimates can be achieved even
under load and temperature uncertainties, making condition based
maintenance of industrial robots possible.
1 Introduction
Industrial robots are used as a key factor to improve productivity, quality and
safety in automated manufacturing. Robot installations are many times of crucial
importance in the processes they are used and an unexpected robot stop or malfunction may lead to production and economical losses. Increased safety, reliability, availability and maintainability (sram) are therefore critical for industrial
robots. Preventive scheduled maintenance is a common approach to guarantee
the requirements on sram in the manufacturing industry. Such scheduling is
103
104
Paper B
Modeling and Identification of Wear in a Robot Joint
often determined from the estimated lifespan of robot components, with considerable margins. Because preventive maintenance is not determined by the actual
robot condition, unnecessary maintenance actions might take place when utilizing this strategy.
In the current scenario, maintainability of industrial robots can be greatly improved with the use of methods to determine its condition, allowing for condition
based maintenance (cbm). With focus on service, it is important that a change
in condition is detected before a critical degradation takes place, so that timely
maintenance actions can take place. Wear in a robot joint may lead to a degradation of performance and to an eventual failure. Because wear typically develops
slowly with time and usage, it might be detectable in an early stage, making cbm
possible.
According to Lansdown et al. (1987) wear can be defined as “the progressive loss
of material from the operating surface of a body occurring as a result of relative
motion at its surface”. The need for relative motion between surfaces implies that
the wear mechanisms are related to mechanical action between surfaces. This
is an important distinction to other processes with a similar outcome and very
different nature, e.g. corrosion (Williams, 2005). Wear is naturally related to friction since friction can be defined as the tangential reaction force between two
surfaces in contact. Friction opposes motion, dissipating kinetic energy. A part
of the work produced by friction appears as heat transfer, vibrations and acoustic emissions. Other outcomes of friction are plastic deformation, adhesion and
fracture which can relate to wear.
The accumulated wear in a tribosystem may lead to variations in friction (Kato,
2000; Bittencourt et al., 2011). Alternatives for wear monitoring are thus possible
provided it is applicable to observe friction and the relation between friction and
wear is known. Monitoring friction to infer about wear is however challenging
since friction is significantly affected by other factors such as temperature and
load. The effects of temperature are specially difficult since temperature is not
measured in typical robot applications. These co-effects should nevertheless be
considered when verifying the reliability of a solution.
In the literature, little can be found about wear estimation for industrial robots.
This may be attributed to the lack of wear models available and the high costs and
time required to perform wear experiments. There are related approaches used
for fault detection, where the objective is to decide whether a change from nominal is present. Faults are typically considered as actuator malfunctions, modeled
as changes in the output torque signals or in the parameters of a robot model.
The latter includes the case of friction changes, which is important since they can
relate to wear.
Considering the nonlinear nature of a manipulator, the use of nonlinear observers
is a common approach for fault detection. Different design approaches have been
suggested, see e.g. Filaretov et al. (1999); McIntyre et al. (2005); Caccavale et al.
1 Introduction
105
(2009); Guo et al. (2012); Dixon et al. (2000); De Luca and Mattone (2003), and
the observer stability is typically guaranteed by analysis of the decay rate of a candidate Lyapunov function. Due to uncertainties in the modeling assumptions, approaches have been suggested to improve robustness of observer-based solutions.
In Brambilla et al. (2008); De Luca and Mattone (2003); Guo et al. (2012), nonlinear observers are used together with adaptive schemes while in Caccavale et al.
(2009), support vector machines are trained to model the uncertainties. A nonlinear fault observer is suggested by Vemuri and Polycarpou (2004) based on a
neural network model for the abnormal robot behavior and defines a robust adaptation rule based on known bounds for the uncertainties. In Ray et al. (2001), an
observer is used to estimate friction torques in a rotating machine; the presence
of a friction change is detected based on a multiple hypothesis test where each
hypothesis is associated to a known friction model.
In Chen (2011), the passivity property of Lagrangian systems is used to define
energy balance equations which are monitored for fault detection and isolation;
the framework is illustrated with a simulation study of a robot manipulator with
faults in dissipative components (e.g. friction changes) and energy-storing components (e.g. load changes). Because the energy balance is also affected by disturbances, knowledge of these effects to the system’s energy can be used to achieve
robustness; some approaches are discussed in Chen (2011), see also Marton (2012).
The vibration patterns generated from a robot joint also contain valuable information about its condition. In Eski et al. (2011), neural networks are used to
learn the vibration patterns of a robot based on accelerometers’ measurements.
Similarly, the acoustic emissions of the robot joints may change under a fault.
In Olsson et al. (2004), features of sound measurements, i.e. peaks of a wavelet
transformation, are monitored and determination of a fault is achieved based on
labeled data using a nearest neighbor classifier. Besides the extra sensors needed,
these approaches require data from a pre-defined trajectory.
The estimation of friction parameters in a robot model from measured data is
a natural approach for fault detection because of the physical interpretation of
these parameters. In Freyermuth (1991), estimates of the Coulomb and viscous
friction parameters are compared to confidence values of their nominal behavior. In the experimental study presented, these parameters could indicate some
of the faults but could not readily distinguish between them; e.g. the increase
of joint temperature had a similar effect as a fault in the drive-chain. The friction model used in Freyermuth (1991) did not consider the effects of temperature
which, as illustrated here, can be larger than those caused by wear. The relation
between temperature and friction was considered in Marton and van der Linden
(2012), where estimates of the viscous friction parameter are used to monitor the
lubricant health in a mechanical transmission. The lubricant temperature is estimated based on a Kalman filter using environment temperature measurements
and a heat transfer model. A similar approach but based on an observer of the
viscous friction torque is also presented in Marton (2011) with simulation studies
for a robot joint.
106
Paper B
Modeling and Identification of Wear in a Robot Joint
In this paper, the effects of wear to friction are studied based on empirical observations. By introducing a variable to describe the effects of wear to friction, an extended friction model is proposed to describe wear-related effects. The suggested
friction model is used to define a maximum-likelihood wear estimator based on
constant-speed friction data which are collected from dedicated experiments, in
an off-line manner. Off-line solutions will decrease the robot availability which
is undesirable. The trade-off between experiment length and the estimator accuracy is therefore important and is studied in detail. The main contributions
leading to the proposed solutions are listed
• first, the effects of wear to friction are modeled based on empirical observations;
• an extended friction model describing the effects of speed, temperature,
load and wear is proposed and identified;
• with a known friction model, maximum likelihood wear estimators are proposed;
• experiment design is considered based on the achievable performance for
any unbiased wear estimator;
• the estimator is validated through simulations and case studies based on
real data.
These results are presented through Sections 2.3 to 5. Sections 2.1 and 2.2 review
earlier results presented in Bittencourt and Gunnarsson (2012) which are used
in this paper; namely, an experiment routine used to provide constant-speed friction data and a friction model to describe the nominal behavior of friction, i.e.
under no significant presence of wear. The conclusions and proposals for further
research are presented in Section 6. The studies presented in the paper are based
on observed friction for joint two of ABB IRB 6620 industrial robots. Joint two
is chosen for the study as it endures great stress variations for the type of robot
considered. The joint is equipped with a rotary vector type of gearbox which is
commonly found in industrial robots of similar sizes.
A preliminary version of a wear estimation approach based on constant-speed
friction data was presented in Bittencourt et al. (2011) where the wear model
was first presented and a wear estimator was suggested and verified. Here, wear
estimators are suggested based on a statistical framework, with a more in-depth
study of experiment design, achievable performance and verification studies.
2 Steady-State Friction in a Robot Joint
Friction is a dynamic phenomenon; at a contact level, the surfaces’ asperities
can be compared to (very stiff) bristles in a brush, each of which can be seen as a
body with its own dynamics connected by the same bulk (Al-Bender and Swevers,
2008; De Moerlooze et al., 2010). Because the internal friction states are not measurable, it is common to study friction in steady-state, when friction presents a
static behavior. As presented in Bittencourt and Gunnarsson (2012), experimental data show that friction data collected under constant speed can be described
2 Steady-State Friction in a Robot Joint
107
by a static nonlinear function.
The simplified behavior of steady-state frictioni facilitates the modeling task and
the determination of the source of changes to friction, e.g. caused by wear or
temperature. A shortcoming is that constant-speed data are not readily available from a robot’s normal operation. This type of data can however be collected
based on the experimental procedure described in Bittencourt and Gunnarsson
(2012). Data collected from such an experiment will be used as input to the wear
estimators suggested here and the procedure is briefly described in Section 2.1.
Using constant-speed friction data, the behavior of friction is studied in detail
in Bittencourt and Gunnarsson (2012) where a static nonlinear model was suggested to describe the effects of speed, temperature and load. This model is reviewed in Section 2.2 and extended in Sections 2.3 and 2.4 to include a description of observed effects caused by wear.
2.1 A procedure to estimate friction at a fixed speed level
A manipulator is a multivariable, nonlinear system that can be described in a
general manner through the rigid multi-body dynamic model
M(ϕ)ϕ̈ + C(ϕ, ϕ̇)ϕ̇ + τ g (ϕ) + τ f = τ
(1)
where ϕ is the vector of motor position, M(ϕ) is the inertia matrix, C(ϕ, ϕ̇) relates to Coriolis and centrifugal terms, τ g (ϕ) are the gravity-induced joint torques
and τ f contains the joint friction components. The system is controlled by the
input torque, τ, applied by the motors (in the experiments the torque reference
from the servo was measured)ii . For single joint movements (so that centrifugal
forces are zero at that joint) and under constant speed (so that inertial torques are
zero), the applied torque at the joint under actuation drives only gravity induced
torques and friction, i.e.
τ g (ϕ) + τ f = τ.
(2)
By considering forward and backward movements for a speed level ϕ̇ around a
configuration ϕ (so that τ g (ϕ) is the same in both directions), a direction independent estimate of friction can be achieved as
τ f = (τ + − τ − )/2
(3)
where τ + and τ − are the resulting torques when the joint is moved forwards,
respectively backwards. In the experiments, each joint is moved separately with
the desired speed ϕ̇ in both directions around a given joint angle ϕ. As an example,
i In this paper, the term steady-state friction is used as a synonym of the friction observed in
constant-speed conditions.
ii It is known that using the torque reference from the controller as a measure of the joint torque
might not always hold. The torque controller for the robot considered in the studies has a steadystate error of maximum 5%. The variations are because of the temperature dependence of the torque
constant of the motors.
Paper B
108
Modeling and Identification of Wear in a Robot Joint
ϕ [rad],
1
ϕ
ϕ̇
ϕ̄
50
τ
τ−
0
0.5
τ+
0
−50
τ
ϕ̇ [rad/s]
100
−0.5
−100
0
2
4
6
t [s]
8
−1
(a) Data collected for the estimation of the friction level at ϕ̇= 42 and ϕ= 0.
τf
0.14
0.12
0.1
fv
0.08
fs
fc
0.06
0
ϕ̇s
50
100
150
ϕ̇ [rad/s]
200
250
(b) Estimated friction levels (circles) and predictions based on (4).
Figure 1: Experimental procedure for the estimation of constant-speed friction. Data are collected for single joint movements back and forth around a
position ϕ for a desired speed ϕ̇, as shown in (a). The constant speed torque
levels for the forward and backward movements, τ + and τ − respectively, are
segmented and used for estimation of τ f according to (3). The procedure
can be repeated for different speed levels and plotted against speed in a friction curve as shown by the circles in (b). The dashed line in (b) corresponds
to predictions computed based on the model in (4) with an interpretation of
the model parameters.
2 Steady-State Friction in a Robot Joint
109
Figure 1a shows the measured joint angle-, speed- and torquei data generated
from such experiment in joint two of an ABB IRB 6620. The constant speed
data are segmented around ϕ and the constant-speed friction levels are achieved
based on (3). The procedure can be repeated for several ϕ̇’s and a friction curve
can be drawn, which contains steady-state friction values plotted against speed,
see Figure 1b. The average time required to execute the trajectory to estimate
friction at one speed was optimized down to 2.5 s.
Friction data collected using such procedure simplifies the determination of wear
related effects since the experiment is performed in a controlled manner, reducing the effects of external disturbances (found e.g. in contact applications) and
it does not depend on a robot model, which may contain uncertainties. The fact
that it does not account for possible direction dependencies of friction is not critical considering that wear would cause a generalized increase of friction which
is captured by (3). Considering that performing experiments with the robot will
reduce its availability, it is important to reduce the number of friction data required to provide accurate wear estimates. As it will be shown, the choice of
which and how many speed levels where friction data are collected are important design parameters, affecting the quality of the wear estimates and the length
of the experiments.
2.2 A model for the nominal behavior of friction
The behavior of friction in a robot joint is considerably affected by other variables
than wear. To allow for a reliable discrimination of wear-related phenomena, it
is therefore important that the effects caused by other variables are well understood. A common description of a direction independent friction curve is given
according to
f
τ (ϕ̇) = f c + f s e
α
ϕ̇
− ϕ̇ s
+ f v ϕ̇
(4)
which is valid for ϕ̇ > 0 and where f c , f s , f v , ϕs and α are model
parameters. The
−
ϕ̇ α
offset term f c is known as the Coulomb parameter; f s e ϕ̇s describes the decay
of friction at intermediate speeds (Stribeck phenomenon) which is common in
lubricated friction and tends to zero with speed according to the Stribeck speed
parameter ϕ̇s and exponent α; the term f v ϕ̇ represents the viscous behavior of
friction, increasing friction at high speeds, see Figure 1b. Based on a comprehensive experimental study of steady-state friction in an industrial robot joint, this
model was extended in Bittencourt and Gunnarsson (2012) to include a descrip-
i Throughout the paper all torques are normalized to the maximum manipulation torque at low
speed and are therefore displayed as dimensionless quantities. All velocity measurements have values
shown in the motor side, before the reduction.
110
Paper B
Modeling and Identification of Wear in a Robot Joint
Table 1: Identified parameters for the model (5), values taken from
Bittencourt and Gunnarsson (2012).
f c,0
f c,ℓ
f s,0
f s,ℓ
f s,ξ
f v,0
3.11 10−2 2.34 10−2 −2.50 10−2 1.26 10−1 1.60 10−3 1.30 10−4
f v,ξ
ϕ̇s,0
ϕ̇s,ℓ
ϕ̇s,ξ
ξVo
α
1.32 10−3
−24.81
9.22
0.98
20.71
1.36
tion of temperature and load according to
f
τ0 (ϕ̇, τ ℓ , ξ)
ℓ
α
ϕ̇ ℓ − ϕ̇s,ℓ = {f c,0 + f c,ℓ τ } + f s,ℓ τ e
{f s,0 + f s,ξ ξ}e
{f v,0 + f v,ξ e
− {ϕ̇
−ξ
ξVo
α
ϕ̇
s,0 +ϕ̇s,ξ ξ} }ϕ̇,
+
+
(5a)
(5b)
(5c)
where τ ℓ is the absolute value of the manipulated load torque and ξ is the joint
temperature, the remaining variables are parameters used to model the friction
behavior. The model (5) extends the parameters f c , f s , ϕ̇s in (4) as a linear function of ξ and τ ℓ , where the exponential terms present a different behavior for
τ ℓ and ξ; the viscous slope parameter f v is extended as a nonlinear function of
ξ. A similar description of load has also been reported for different devices by
Hamon et al. (2010); Kammerer and Garrec (2013). Marton and van der Linden
(2012) also reported an exponential dependency of viscous friction with temperature.
In Bittencourt and Gunnarsson (2012), the parameters in (5) were found for joint
two of an ABB IRB 6620 industrial robot with the use of joint temperature measurements and an estimate of τ ℓ based on a robot model; the parameter values
are given in Table 1. Figure 2a presents observed and model-based predictions
of friction curves for high and low values of τ ℓ and ξ. Notice the effects of τ ℓ ,
which give an offset increase of the whole curve together with an exponentiallike increase at speeds below 25 rad/s. The effects of ξ can be seen as an exponential increase at speeds below 80 rad/s and a decrease of the curve slope at higher
speeds. Notice further that for such temperature and load values, there is a speed
range where the effects are less pronounced, in this case around 80 rad/s.
Validation
As shown in Bittencourt and Gunnarsson (2012), the model in (5) can be used
to predict the behavior of steady-state friction under broad operation conditions.
This model can thus be used as a description of the nominal behavior of friction.
The mean and standard deviation of the prediction error for the model in (5),
denominated here as ǫ, were estimated based on more than 5800 steady-state
friction data points collected under different speed, temperature and load conditions as [µǫ , σǫ ] = [−9.24 10−4 , 4.23 10−3 ]. The same evaluation for a model based on
(4), dependent only on speed, gave a mean and standard deviation for the error
2 Steady-State Friction in a Robot Joint
0.2
ξ
ξ
ξ
ξ
τf
0.15
= 33◦ C,
= 80◦ C,
= 33◦ C,
= 80◦ C,
111
τℓ
τℓ
τℓ
τℓ
= 0.70
= 0.70
= 0.01
= 0.01
0.1
0.05
0
0
50
100
150
ϕ̇ [rad/s]
200
offset: 0.038
250
300
(a) Observed friction curves (markers) and model-based predictions (lines) given
by (5) for low and high values of ξ and τ ℓ and no significant wear.
0.12
0.1 0
20
40
60
80
100
τf
0.08
0.06
0.04
0.02
0
0
50
100
150
ϕ̇ [rad/s]
offset: 0.017
200
250
(b) Wear effects from accelerated tests. The colormap is related to the length of the
tests with values between 0 and 100. The dashed line relates to a wear level critical
for cbm.
Figure 2: Friction dependencies in a robot joint based on experimental studies. The offset values were removed for a comparison, their values are shown
in the dotted lines. The data were collected for similar gearboxes and are
presented in directly comparable scales. Notice the larger amplitude of effects caused by temperature and load compared to those caused by wear but
the different speed dependencies.
112
Paper B
Modeling and Identification of Wear in a Robot Joint
as [1.09 10−2 , 1.34 10−2 ] which are considerably larger.
2.3 A model for the effects of wear to friction
Monitoring a robot until a failure takes place is a costly and time consuming task
and it is thus difficult to fully comprehend the effects of wear in a robot joint.
An alternative is considered here based on data collected from accelerated wear
tests, where the robot is run continuously under high load and stress levels for
several months or years until failure. The resulting friction curves from such
experiment at joint two of an ABB IRB 6620 robot are shown in Figure 2b, which
were obtained under constant or nearly constant load- and temperature levels.
The different speed dependencies of these effects compared to those caused by
temperature and load in Figure 2a is an important characteristic of the problem.
It shows that a careful selection of speed levels is needed to obtain an accurate
determination of wear based on friction data.
Resolving for coupled effects between wear, temperature, load and other parameters would require costly long term experiments which are inviable even for
accelerated tests. A simplifying assumption is taken that considers the effects of
load and temperature to be additively separable from those caused by wear. Under this assumption, the effects of wear can be isolated in friction data collected
under constant load and temperature conditions, such as the friction curves of
Figure 2b. From such data, a wear profile quantity, e
τ f , is defined by subtractf
ing nominal friction data, observed before the accelerated wear tests started, τ0 ,
from the ones obtained thereafter i.e.,
f
e
τ f , τ f − τ0 .
(6)
The resulting wear profile from the accelerated wear tests in Figure 2b can be
seen in Figure 3, where friction is presented along speed ϕ̇ and the experiment
length k with values between 0 and 100.
As can be noticed, the effects of wear appear as an increase of friction in the low
to intermediate speed region, and a small decrease of the viscous friction velocity
slope. Introducing ̟ as a wear parameter, the observations support the choice of
a model structure for the wear profile as
e
τ f (ϕ̇, ̟) = f s,̟ ̟e
ϕ̇ α
− ϕ̇ ̟ s,̟
+ f v,̟ ̟ ϕ̇,
(7)
where f s,̟ , ϕ̇s,̟ , f v,̟ and α are model parameters. Except for the Coulomb term,
the model has a similar structure as (4) with coefficients dependent on ̟. The
variable ̟ relates to the degree of which the wear effects appear in the observed
friction and it is not a physical quantity nor can it be measured. The wear parameter ̟ is defined by convention with values in the interval (0, 100), relative to a
failure state, and is a dimensionless quantity.
The value ̟ = 100, denoting a failure state, should be defined as the point where
the robot fails to perform according to the requirements imposed by the application. Clearly, a failure is always present in case the robot condition leads to a
2 Steady-State Friction in a Robot Joint
113
0.08
0.06
f
e
τf
τ0
0.04
0.02
0
0
50
100
ϕ̇ [rad/s]
150
200
250
300
20
40
80
60
100
k
Figure 3: Friction wear profile e
τ f computed from the data in Figure 2b acf
cording to (6). The dotted line relates to the nominal friction curve τ0 removed from the friction data. The dashed line indicates a wear level considered important to be detected.
robot stop. While a robot stop may be caused by a total mechanical failure of the
gearbox components, a more common situation is to have stops triggered by the
safety supervision of the robot. The safety supervision may be triggered due to
the presence of torque levels exceeding a maximum allowed level. This torque
limit can thus be used to find the level of ̟ which would cause a robot stop by
the safety supervision system.
In order to allow for condition based maintenance, the wear changes should be
detected before a failure takes place. In fact, an alarm should be generated early
enough so that appropriate maintenance actions can take place with minimal
interference. Because wear will develop with time depending on how the robot is
used, it is difficult to determinate a priori a critical wear level to be detected. This
can be addressed with the development of lifetime models for prognosis which
is outside the scope of this work. Often, lifetime models are developed based on
the statistical behavior of failure data (Chick and Mendel, 1996) and are typically
found during product development.
Identification
The model in (7) is identified with the wear profile data of Figure 3. For these
data, a robot stop triggered by the safety supervision occurs at k = 100 which is
considered as a failure state. Based on a lifetime model developed for this robot,
the robot manufacturer decided that in order to allow for cbm, it is critical to
detect the wear level at k = 96.77. Because it is important that the wear model
is most accurate for this critical level, the data collected at k = 96.77 are used
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Table 2: Parameters for the model in (7) and one standard deviation identified using the wear profile data at k = 96.77 with wear fixed at ̟ = 35.
f s,̟ [ 10−4 ]
f v,̟ [ 10−7 ]
ϕ̇s,̟
9.02 ± 0.19 −5.15 ± 1.00 2.19 ± 0.15
b = 22.39, k = 94.62
̟
b = 30.35, k = 95.70
̟
b = 35.00, k = 96.77
̟
b = 46.73, k = 97.85
̟
0.04
0.03
e
τf
0.02
0.01
0
0
50
100
150
ϕ̇ [rad/s]
200
250
Figure 4: Observed wear profile data (circles) and model predictions (lines).
for the identification of the parameters of the wear model under the convention
that ̟ = 35. This convention is adopted because the value of e
τ f at k = 96.77 and
ϕ̇ = 28 rad/s is around 35% of the maximum value of e
τ f for the entire data, which
occurs at k = 100 and same speed. The parameter α is fixed to 1.36 for consistency
with the parameters found for (5), given in Table 1. The identification method
described in Bittencourt and Gunnarsson (2012) is used to find the remaining
parameters, which are shown in Table 2.
Validation
Considering the identified parameters for the model in (7), the wear levels of Figure 3 are identified for each k. With the identified wear values, the wear profile
given by model predictions from (7) and observations are presented for the interval k ∈ (94, 98) in Figure 4. As can be noticed, the model can predict well the
behavior of e
τ f . The estimated mean and standard deviation for the prediction er−4
−3
ror of the wear model in (7), denoted here as e
ǫ, are [µe
ǫ , σe
ǫ ] = [9.72 10 , 3.82 10 ].
2.4 A complete model of steady-state friction
Under the assumption that the effects of load/temperature and wear are additively separable, it is possible to extend the model given in (5) to include the
effects of wear as
f
τ f (ϕ̇, τ ℓ , ξ, ̟) = τ0 (ϕ̇, τ ℓ , ξ) + e
τ f (ϕ̇, ̟),
(8)
3 Model-Based Wear Estimation
0
20
115
40
60
100
80
τf
0.15
0.1
0.05
0
50
100
150
ϕ̇ [rad/s]
200
250
Figure 5: Friction curves for different wear levels given by the model (8)
with temperature and load fixed at ξ = 40◦ C and τ ℓ = 0.10 respectively. The
colormap indicates ̟ and the dashed line relates to the critical wear level
̟ = 35.
f
where τ0 (ϕ̇, τ ℓ , ξ) is given by (5) and e
τ f (ϕ̇, ̟) is described in (7). Figure 5
presents the friction values given by the proposed model for ξ = 40◦ C and τ ℓ =
0.10 for wear values in the interval ̟ ∈ (0, 100) when the parameters given in
Tables 1 and 2 are used. Notice that the effects are appear first in the speed range
between 0 − 150 rad/s and also that the resulting friction curves show good resemblance to Figure 2b. As previously, the dashed line in Figure 5 indicates an
alarm level for the wear with ̟ = 35.
3 Model-Based Wear Estimation
Consider that the experiment described in Section 2.1 is repeated N times independently at the speed levels
h
iT
ϕ̇ = ϕ̇1 , ϕ̇2 , · · · , ϕ̇N
resulting in the steady-state friction data points
h
i
f T
τ f = τ1f , τ2f , · · · , τN
.
To illustrate the situation where these experiments are performed at once, during
the execution of a test-cycle, it is considered that the load, temperature and wear
conditions are the same for these data. A model for each ith steady-state friction
f
datum τi can be achieved by including an additive uncertainty term to the model
in (8). Assuming that the prediction errors for models (5) and (7) follow indepen2
dent Gaussian distributions, ǫ ∼ N (µǫ , σǫ2 ) and e
ǫ ∼ N (µe
ǫ , σe
ǫ ), the resulting data
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generation model is
f
f
τi = τ0 (ϕ̇i , τ ℓ , ξ) + ǫ + e
τ f (ϕ̇i , ̟) + e
ǫ = τ f (ϕ̇i , τ ℓ , ξ, ̟) + ǫ
(9a)
where the resulting noise properties are given as
ǫ ∼ N (µǫ, σ 2 ),
ǫ
2
σ 2 = σǫ2 + σe
ǫ.
ǫ
µǫ = µǫ + µe
ǫ,
Considering µǫ ≈ 0, the joint density function for the friction data τ f is
p(τ f |τ ℓ , ξ, ̟) = N τ f ; τ f (ϕ̇, τ ℓ , ξ, ̟), Σ
where Σ = I σ 2 and
ǫ
h
τ f (ϕ̇, τ ℓ , ξ, ̟) = τ f (ϕ̇1 , τ ℓ , ξ, ̟),
τ f (ϕ̇2 , τ ℓ , ξ, ̟),
··· ,
τ f (ϕ̇N , τ ℓ , ξ, ̟)
(9b)
(10)
iT
.
An unbiased estimate of the load torque τ ℓ is considered available (e.g. achieved
using a robot model) with distribution N (τ ℓ ; µℓ , σℓ2 ). The information from this
estimate is included in the model by considering the marginal density function
f
p(τ |ξ, ̟) =
Z∞
−∞
p(τ f |τ ℓ , ξ, ̟)N (τ ℓ ; µℓ , σℓ2 ) dτ ℓ
(11)
which for the Gaussian distribution p(τ f |τ ℓ , ξ, ̟) given in (10) can be found explicitly since the dependence of the mean τ f (ϕ̇, τ ℓ , ξ, ̟) is linear on τ ℓ , recall (5).
The marginal density function is given by (Bishop, 2006, p. 93)
p(τ f |ξ, ̟) = N τ f ; τ f (ϕ̇, ξ, ̟), Σ(ϕ̇)
(12)
where
τ f (ϕ̇, ξ, ̟) , τ f (ϕ̇, µℓ , ξ, ̟)
(13)
Σ(ϕ̇) = Σ + s(ϕ̇)s(ϕ̇) σℓ2
h
iT
s(ϕ̇) , s(ϕ̇1 ), s(ϕ̇2 ), · · · , s(ϕ̇N )
T
s(ϕ̇) , f c,ℓ + f s,ℓ e
ϕ̇ α
− ϕ̇ s,ℓ
(14)
.
It is further considered that the model parameters are known. In this setting, the
vector of unknowns is θ = [ξ, ̟]T and has the log-likelihood function
log L(θ) = log N τ f ; τ f (ϕ̇, θ), Σ(ϕ̇) .
(15)
Based on the achieved likelihood function, Section 3.1 discusses maximum likelihood estimators of ̟. The estimate is dependent on the data τ f and thus on
the choice of speed levels ϕ̇. For a limited number of friction observations N , the
problem of experiment design is to choose ϕ̇ such that the estimated wear level
is as accurate as possible. Experiment design is described in Section 3.2.
3 Model-Based Wear Estimation
117
3.1 Maximum likelihood estimation
The maximum likelihood estimate of θ given the data vector τ f is the value for
which the log-likelihood function, given in (15), has a maximum, i.e.
b
θ = arg max log L(θ).
θ
The terms dependent on θ in the log-likelihood function have the form
h
iT
h
i
log L(θ) ∝ − τ f − τ f (ϕ̇, θ) Σ(ϕ̇)−1 τ f − τ f (ϕ̇, θ) ,
and the problem is therefore a weighted nonlinear least-squares, where ξ and
̟ are estimated jointly. To restrict the search space, it is possible to add constraints to the problem according to available knowledge of the unknowns. Naturally, ̟ ≥ 0, and it is also possible to include lower and upper limits for the
temperature, denoted ξ and ξ respectively. For a robot operating in a controlled
indoor environment, ξ would be the minimum room temperature while ξ is given
by the maximum room temperature and self heating of the joint due to actuator
losses. This gives the problem
h
iT
h
i
b̟
b] = arg min τ f − τ f (ϕ̇, ξ, ̟) Σ(ϕ̇)−1 τ f − τ f (ϕ̇, ξ, ̟)
[ξ,
ξ,̟
s.t.
(16)
0 ≤̟
ξ ≤ξ ≤ ξ,
which is solved using lsqnonlin available in Matlab’s Optimization Toolbox
with initial values found from a coarse grid search.
The estimator in (16) is valid for N ≥ 2 since at least two equations are needed to
solve for the two unknowns. For N = 1, the effects of temperature can be marginalized away. Considering that temperature ξ can occur with equal probability over
its domain, i.e. ξ ∼ U (ξ, ξ), the marginalized likelihood function is,
f
p(τ |̟) =
1
ξ−ξ
Zξ
p(τ f |ξ, ̟) dξ.
(17)
ξ
Since there is no analytical solution for (17), Monte Carlo Integration (MCI) is
used to approximate it in a symbolic expression in ̟ as
Nξ
1 X f
b
p(τ |, ξ (i) , ̟)
p (τ |̟) =
Nξ
f
(18)
i=1
for Nξ randomly generated samples ξ (i) ∼ U (ξ, ξ).
Using the approximated marginalized likelihood function of (18) leads to the
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118
Modeling and Identification of Wear in a Robot Joint
problem
b = arg max log b
̟
p (τ f |̟)
(19)
̟
s.t. 0 ≤ ̟.
This nonlinear constrained minimization is solved using fmincon from the Optimization Toolbox in Matlab with initial values taken from a coarse grid search.
3.2 Experiment design
An estimate b
θ of θ is dependent on the data τ f , the associated ϕ̇ and on the
estimator used. The mean square error (mse) of an estimate can be used as a
criterion to assess how the choice of ϕ̇ affects the performance. Let the bias of an
estimate b
θ be denoted b(b
θ) , E[b
θ] − θ′ then
h
i
MSE(b
θ) = E (b
θ − θ′ )T (b
θ − θ′ ) = tr Cov(b
θ) + b(b
θ)T b(b
θ).
(20)
Considering unbiased estimators, the Cramér-Rao lower bound for unbiased estimators gives
MSE(b
θ) ≥ tr F (θ′ )−1
(21)
where

!
!  ∂ log L(θ) ∂ log L(θ) T 
F (θ ) , E 
∂θ
∂θ
′




′
(22)
θ =θ
is the Fisher information matrix. The achieved bound can be minimized by affecting the inverse of the Fisher information matrix, improving the achievable
performance for any unbiased estimator. For the log-likelihood function in (15),
the Fisher information matrix is given by (Porat and Friedlander, 1986)
!T
!
∂τ f (ϕ̇, θ)
∂τ f (ϕ̇, θ) F (ϕ̇, θ′ ) =
(23)
Σ(ϕ̇)−1
∂θ
∂θ
θ =θ ′
where the dependence on ϕ̇ is highlighted.
The objective of the experiment is to determine the wear level ̟ as accurately
as possible. This can be achieved by an appropriate choice of the speed levels
b)
ϕ̇, which are the design variables. For θ = [ξ, ̟]T , the lower bound on MSE(̟
corresponds to the 2,2-element of the inverse of the information matrix given
by (23). Using this bound as a criterion for experiment design gives the problem
ϕ̇ ∗ = arg min [F (ϕ̇, θ′ )−1 ]2,2 ,
ϕ̇
(24)
where [ · ]i,j denotes the i, j-element of a matrix. Dropping the arguments for
F (ϕ̇, θ′ ), the analytical expression for [F −1 ]2,2 is given by
[F −1 ]2,2 =
[F ]1,1
[F ]1,1 [F ]2,2 − [F ]21,2
.
(25)
4 Simulation Study
119
∂τ f (ϕ̇ ,θ )
has rank
For a positive definite Σ(ϕ̇), the problem is well-posed only if
∂θ
equal to the number of unknowns, i.e. two. This can only be achieved for N ≥ 2
∂τ f (ϕ̇ ,θ )
and if there are at least two linear independent columns in
. A necessary
∂θ
condition for the latter is that at least two of the speed levels chosen are different,
sufficiency will depend on the specific choice of friction model and value of θ′ . To
ensure that no same speed is chosen, additional constraints are included in (24) to
require a minimum separation, δϕ̇ , between speed levels in ϕ̇. Furthermore, the
search is limited to the minimum, ϕ̇, and maximum, ϕ̇, speed levels for which
the experiment of Section 2.1 can be performed. The optimal speed values are
therefore given as the solution to the problem
ϕ̇ ∗ = arg min [F (ϕ̇, θ′ )−1 ]2,2
ϕ̇
s.t.
(26)
ϕ̇i − ϕ̇j ≤ −δϕ̇ , (i < j)
ϕ̇ ≤ ϕ̇i ≤ ϕ̇
This is a constrained nonlinear minimization which is solved here using fmincon
in Matlab with initial values found from a coarse grid search.
The case where N = 1 can be considered by using the approximated marginalized
likelihood function given by (18). Using this approximation, the Fisher information matrix is


! f |̟) 2 
 ∂ log b
p
(τ
′
 .

F (ϕ̇, ̟ ) , E 

∂̟
′
̟=̟
p ( · ) is performed symbolically and the expectation is comThe differentiation of b
puted using MCI with Nf samples taken from b
p (τ f |̟) in (18), leading to the
b(ϕ̇, ̟′ ) of F (ϕ̇, ̟′ ). The associated optimization problem is thus
estimate F
b(ϕ̇, ̟′ )−1
ϕ̇ ∗ = arg min F
ϕ̇
s.t.
(27)
ϕ̇ ≤ ϕ̇i ≤ ϕ̇
which is also a constrained nonlinear minimization problem and is solved in the
same manner as (26).
4 Simulation Study
A simulation study is first considered to illustrate the use of the experiment design criteria defined in Section 3.2 and wear estimators proposed in Section 3.1.
4.1 Definition of parameters used
The framework of Section 3 requires knowledge of the friction model parameters
in the data generation model (9). The parameters for the nominal part given
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Table 3: Optimization parameters.
Experiment design Identification Approximations
ϕ̇
ϕ̇
δϕ̇
ξ
ξ
Nξ
Nf
1 280
5
30
50
100
200
Table 4: Choice of optimal speed values for different values of N . “Cost” is
the value of the objective function in (27) (N = 1) or (26) (N ≥ 2) computed
at ϕ̇ ∗ .
N
Cost
ϕ̇ ∗
1 45.91
33.78
2 26.01
[35.84, 40.84]T
3 19.65
[33.68, 38.68, 43.68]T
4 16.50 [31.65, 36.65, 41.65, 46.65]T
in (5) can be identified for a new robot using joint temperature measurements
and an estimate of the joint load torques, see e.g. Bittencourt and Gunnarsson
(2012). The parameters for (7), describing the wear behavior, are more difficult
because failure data are required. For cbm, wear estimates are needed before
a failure of the system, in which case the parameters for (7) cannot be known
in advance. This can be overcome with the use of historical failure data. The
simulation studies that follows illustrate the case where these model parameters
are known, focusing on the effects of temperature and load uncertainties. In
Section 5, the effects of uncertainties in the wear model are studied based on real
data.
Here, the friction parameters used are given in Tables 1 and 2 which were identified for joint two of an ABB IRB 6620 industrial robot. The noise properties of
(9b) are taken from the model validation in Sections 2.2 and 2.3. Applying (9b)
to these values gives µǫ = 4.80 10−5 ≈ 0 and σǫ = 5.70 10−3 . The mean and standard
deviation for the load estimate used in (11) are chosen as µℓ = 0.5 and σℓ = 0.1.
Finally, the optimization parameters used in the identification and experiment
design problems are given in Table 3.
4.2 Experiment design
As discussed in Section 3, the objective of experiment design is to choose ϕ̇ that
gives as high accuracy as possible for the wear estimate. From a practical perspective, it is also important to limit the number of friction data points N . Here,
the experiment design will be considered for N ∈ {1, 2, 3, 4}. For friction data
collected according to the experiment defined in Section 2.1, this would give up
to one minute of total experimentation time for a six axis robot. The problems
(27) and (26) are solved for N = 1 and N ∈ {2, 3, 4} respectively when temperature
and wear are fixed to ξ = 40◦ C and ̟ = 35. The optimal speed values found are
shown in Table 4, which have values in the interval (30, 50) rad/s.
4 Simulation Study
50
45
ξ [◦ C]
2.5
2
1.5
1
0.5
0
121
|g ξ (ϕ̇, ξ)|
|g ̟ (ϕ̇, ̟)|
0
40
35
30
10 20 30 40 50 60 70 80
ϕ̇ [rad/s]
50 100 150 200 250
ϕ̇ [rad/s]
(a) Absolute value of the derivatives
|g ξ (ϕ̇, ξ)| and |g ̟ (ϕ̇, ̟)|.
(b) Speed region where |g ̟ (ϕ̇, ̟)|
2|g ξ (ϕ̇, ξ)|.
>
Figure 6: (a) Behavior of g ̟ (ϕ̇, ̟) and g ξ (ϕ̇, ξ) with respect to speed evaluated at ξ = 40◦ C and ̟ = 35. (b) The speed regions which give |g ̟ (ϕ̇, ̟)| >
2|g ξ (ϕ̇, ξ)| when ̟ = 35 and ξ ∈ (30, 50)◦ C.
To provide more insights on the experiment design problem, note that the information matrix used in the optimal solution for (26) is dependent on products of
the derivatives
∂τ f (ϕ̇, ξ, ̟) ∂τ f (ϕ̇, ξ, ̟) ̟
′
g ξ (ϕ̇, ξ ′ ) ,
g
(
ϕ̇,
̟
)
,
,
(28)
′
∂ξ
∂̟
ξ=ξ
̟=̟′
which, because of the model structure, recall (13) and (8), are function only of ϕ̇
and the differentiation variable. These derivatives relate to the information about
temperature, ξ, and wear, ̟, contained in the model. For instance, if Σ(ϕ̇) = I ,
the objective function [F −1 ]2,2 in (25) is
N
X
g ξ (ϕ̇i , ξ)
2
i=1
 N
 N
2 .
N
X
X





X
2 
2 

g ̟ (ϕ̇i , ̟)  − 
g ξ (ϕ̇i , ξ)g ̟ (ϕ̇i , ̟)
g ξ (ϕ̇i , ξ)  

i=1
i=1
i=1
The objective of the experiment design is to gather information about ̟, i.e.
minimize [F −1 ]2,2 , and it is hence natural that speed points are selected where
|g ̟ (ϕ̇, ̟)| is larger than |g ξ (ϕ̇, ξ)|. These gradients are shown in Figure 6a as a
function of speed ϕ̇ for the fixed ξ = 40◦ C and ̟ = 35. For speed levels in the
interval (30, 50) rad/s, it is possible to note that |g ̟ (ϕ̇, ̟)| is always at least twice
as large as |g ξ (ϕ̇, ξ)|.
The model derivatives (28) are dependent on the operating point for ξ and ̟ and
it is not possible to select ϕ̇ that is optimal globally. To illustrate these dependencies, Figure 7 shows contour plots of |g ξ (ϕ̇, ξ)| and |g ̟ (ϕ̇, ̟)| for a wide range of
speed, temperature and wear values. The dashed lines in Figure 7 relate to the
value where the derivatives are zero. In both sub figures, the derivatives have
negative values to the right of the dashed lines and are otherwise positive. The
ξ [◦ C]
80
1
2
50
100 150 200 250
ϕ̇ [rad/s]
60
3
4
5
Modeling and Identification of Wear in a Robot Joint
6
×10−3
40
20
100
80
60
40
20
0
2
4
6
̟
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122
50
8
×10−4
100 150 200 250
ϕ̇ [rad/s]
Figure 7: Contours of |g ξ (ϕ̇, ξ)| (left) and |g ̟ (ϕ̇, ̟)| (right) as a function of
speed and the differentiation variable. Notice the difference in the scales.
derivative for temperature is often larger than that for wear. Nevertheless, the
different speed dependencies allow for a selective choice of ϕ̇ to improve performance of the wear estimates.
To illustrate how the optimal speed region can vary with operating points, Figure 6b displays the speed region where |g ̟ (ϕ̇, ̟)| > 2|g ξ (ϕ̇, ξ)| when ̟ = 35, i.e.
the critical value to be detected, and ξ ∈ (30, 50)◦ C. Notice that this speed region
is not optimal in the sense of (26) or (27), but relates to a region where the information for ̟ is considerably larger than for ξ. As it can be seen, only a narrow
band of speed values contain useful information for the estimation of ̟. The
speed band also varies with temperature, with no overlap over all temperature
values considered.
It is important to emphasize that the characteristics shown here are valid for the
specific model parameters used and different properties are expected for different robots and gearboxes. Nevertheless, a similar behavior of temperature and
wear has been observed for various robot units equipped with a similar type of
gearbox.
4.3 Bias and variance properties of the wear estimators
With the optimal speed values found in Table 4, the bias and variance properties
of the proposed estimators are assessed based on Monte Carlo simulations. The
true wear level is fixed at ̟ = 35 and temperature is varied in the interval ξ ∈
(30, 50)◦ C. The data generated by (9) are input to (19) or (16) for N = 1 and N ∈
{2, 3, 4} respectively and the estimation is repeated a total of NMC = 1 103 times per
operating point.
Figure 8 shows the simulation results for the bias and variance of the estimators
as a function of the temperature level ξ. As it can be seen, the bias and variance
are reduced with N . The reduction in the variance is specially large for N = 2
compared to N = 1, which is related to marginalization effects of ξ. The bias
presents a nonlinear behavior with ξ while the variance seems unaffected by it.
5 Studies based on Real Data
50
30
35
40
ξ [◦ C]
45
variance
N =1
N =2
N =3
N =4
bias
4
2
0
−2
−4
123
50
N =1
N =2
N =3
N =4
40
30
20
10
30
35
40
ξ [◦ C]
45
50
Figure 8: Monte Carlo based estimates of the bias (left) and the variance (right) for the wear estimators (19) and (16) (N = 1 and N ∈ {2, 3, 4}
respectively) evaluated for ̟ = 35 and ξ ∈ (30, 50)◦ C.
5 Studies based on Real Data
Gathering enough informative data related to wear from the field would have
been inviable since wear faults take a long time to develop and are infrequent.
Even in accelerated wear tests, it may take several months or years before wear
effects become significant. Another difficulty with such tests is the high cost of
running several robots to obtain reliable statistics. Temperature studies are also
challenging since the thermal constant of a large robot is of several hours.
Other than simulation studies, the only viable alternative in the research project
was to combine nominal friction data (with no acute wear present) and wear profile data collected from a different robot of the same type. These data were collected from axis two of ABB IRB 6620 industrial robots equipped with rotary
vector gearboxes. Each of these data sets are matrices where each row contains
data from a friction curve collected in the following velocity values
ϕ̇ = [2.1, 8.7, 15.3, 21.9, 28.5, 35.1, 41.7,
82.2, 133.5, 184.7, 236.2, 287.1],
(29)
i.e., a total of twelve different speed values are possible from these data sets. The
nominal friction data matrix, F 0 , has rows associated to different load and temperature conditions which are stored in matrices of the same size as F 0 denoted L
e contains data collected from
and T respectively. The wear profile data matrix, F,
accelerated wear tests under constant load and temperature conditions and has
rows associated to the experimentation index k .
The wear profile data set determines the behavior of friction as a function of k.
For a given wear profile data set, the objective is to emulate friction data collected
under varying conditions of load and temperature. First, the desired load and
temperature behaviors are pre-defined as a function of k according to ξ(k) ∈ T
and τ ℓ (k) ∈ L. Second, the data sets are combined as a function of k and the
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Modeling and Identification of Wear in a Robot Joint
desired speed ϕ̇ ∈ ϕ̇ to provide friction data according to
e k,j
τ f (k, ϕ̇) = [F 0 ]ik ,jϕ̇ + [F]
ϕ̇
n
o
jϕ̇ , j : [ϕ̇]j = ϕ̇ ,
n
o
ik , i : [T ]i,jϕ̇ = ξ(k) and [L]i,jϕ̇ = τ ℓ (k) .
(30a)
(30b)
(30c)
Notice that these data are not analytically generated, but actually based on friction data collected with the experiment described in Section 2.1. Furthermore,
the combination of data according to (30a) is consistent to the model structure
in (8) and with the assumption that the effects of load/temperature and wear are
additively separable.
5.1 Description of scenarios
e0 , F
e1
Three different wear profile data sets are considered, they are assigned as F
2
0
e . The data set F
e was used for the wear modeling presented in Section 2.3,
and F
and is shown in Figure 3 and the other two are shown in Figure 9. Some relevant
characteristics of the wear profile data sets are listed next where the quantity
rmax (F, G) ,
max |Fij |
ij
max |Gij |
ij
denotes the ratio of maximum absolute values of the matrices F and G and is
used for a comparison.
e0 Presents small random variations, remaining around 0 up to k = 90 followed
F
by an exponential increase thereafter.
e1 Presents medium random variations, remaining around 0 up to k = 70 folF
e0 it gives rmax (F
e1 , F
e0 ) = 56%.
lowed by large increases. Relative to F
e2 Presents small random variations, remaining stationary up to k = 30 followed
F
by small increases up to k = 97 from where it increases steeply. Relative to
e0 it gives rmax (F
e2 , F
e0 ) = 106%.
F
e0 ),
Three scenarios are considered in the study based on the data set pairs (F 0 , F
0
1
0
2
e
e
(F , F ) and (F , F ). The scenarios are denoted 0, 1 or 2 according to the selected
data set for the wear profile. The same model and optimization parameters considered in Section 4.1 are used for all scenarios. The parameters for the friction
e0 . Since these paramemodel were identified based on the data sets F 0 and F
ters are used for all scenarios, it can be considered that the parameters for the
nominal behavior of friction are consistent for all scenarios. This illustrates the
situation where the nominal friction parameters are found based on experiments
performed when the joint is healthy. The wear-related parameters are only consistent for Scenario 0 and the other scenarios illustrate the situation where the
wear parameters are based on historical failure data.
5 Studies based on Real Data
125
Table 5: Choice of optimal speed values for different values of friction observations N .
N
Cost
ϕ̇ ∗
1 46.58
35.1
2 26.20
[35.1, 41.7]T
3 22.60
[28.5, 41.7, 82.2]T
4 18.00 [2.1, 28.5, 35.1, 41.7]T
To simplify the presentation of the results, the behavior of ξ(k) and τ ℓ (k) are
the same for the three scenarios, they are shown in Figure 10. Notice that the
amplitude of the friction changes due to temperature and load are considerably
larger than of those caused by wear for any of the scenarios.
5.2 Results and discussion
The choice of speed values for experiment design is limited to the speed levels available from the data sets as given in (29). The problems (26) and (27)
are solved by considering every possible combination of speed levels for N ∈
{1, 2, 3, 4}. The resulting optimal values are given in Table 5 and relate well to
those found in Table 4. Notice that the optimal speed values depend on the wear
e0 , optimality
model parameters used. Since these parameters were found using F
∗
of ϕ̇ is only expected for Scenario 0.
The resulting wear estimates for the different scenarios are shown in Figures 11a
to 11c. The shaded areas in the figures highlight a region which should be easily
distinguishable from the rest in order to allow for an early detection of excessive
wear. Noticeably, the wear estimates are consistent to the wear profile data used
in all scenarios, even for Scenarios 1 and 2 when the wear model is uncertain. For
each ith scenario, the wear estimates achieved at k = 100 show good corresponei , F
e0 ). These observations indicate positively to the viability of the
dence to rmax (F
determination of the wear related parameters based on historical data.
The wear estimates become smoother for larger N , which is in line with the simulation study of Section 3.1. For all scenarios, the larger wear estimates for k > 90
allows for a distinction of the critical (shaded) regions. For Scenarios 0 and 1,
the detection of a critical wear change could be achieved with a threshold set
at the critical value of 35. The same threshold would however give an early detection for Scenario 2. An early detection is understood as less critical than a
failure but may lead to unnecessary maintenance actions. A more careful analysis of the wear estimates may therefore be needed in order to support accurate
maintenance decisions.
The fact that the wear estimates do not differ much with N might lead to the conclusion that N = 1 should be used, but the behavior of the estimates degrade if
non-optimal speed levels are chosen. To illustrate this, two wear estimates were
achieved for Scenario 2 using only one measurement at ϕ̇ = 82.2 and ϕ̇ = 133.5.
Paper B
126
f
τ0
e
τf
0.05
0.04
0.03
0.02
0.01
0
−0.01
0
Modeling and Identification of Wear in a Robot Joint
50
100
ϕ̇ [rad/s]
150
200
250
300
0
20
40
60
80
100
k
e1 .
(a) Wear profile data F
f
τ0
e
τf
0.1
0.08
0.06
0.04
0.02
0
−0.02
0
50
100
ϕ̇ [rad/s]
150
200
250
300
0
20
e2 .
(b) Wear profile data F
40
60
80
100
k
Figure 9: Friction wear profile data used in Scenarios 1 (a) and 2 (b). The
dashed lines indicate a critical wear level to be found. The dotted lines relate
f
to the nominal friction curve τ0 that was removed from the friction data
according to (6).
τf
5 Studies based on Real Data
0.16
0.13
0.1
0.07
0.037
0
50
100
150
ϕ̇ [rad/s]
127
200
250
300
0
40
20
80
60
100
k
(a) Nominal friction behavior for all scenarios.
45
0.8
τℓ
ξ 0.6
40
0.4
35
0.2
30
0
τℓ
ξ [◦ C]
50
20
40
k
60
80
0
100
(b) Associated temperature, ξ , and load, τ ℓ , values.
Figure 10: (a) Behavior of nominal friction as a function of ϕ̇ and k for the
scenarios considered; an offset value corresponding to the smallest friction
value in the data set was removed for a comparison to the wear profile data.
The associated temperature and load values are shown in (b).
Paper B
128
0
N =1
N =2
N =3
N =4
20
100
80
60
40
20
0
b
̟
b
̟
100
80
60
40
20
0
Modeling and Identification of Wear in a Robot Joint
40
k
60
80
100
0
(a) Scenario 0.
0
40
k
60
80
100
(b) Scenario 1.
N =1
N =2
N =3
N =4
20
20
100
80
60
40
20
0
b
̟
b
̟
100
80
60
40
20
0
N =1
N =2
N =3
N =4
40
k
60
(c) Scenario 2.
80
100
0
20
40
Φ̇ = 82.2
Φ̇ = 133.5
Φ̇ = [82.2, 133.5]T
k
(d) Scenario 2 with non-optimal speed values.
Figure 11: Wear estimates for the different scenarios investigated. Figures (a)
to (c) present the estimates for N ∈ {1, 2, 3, 4} using the optimal speed values.
Figure (d) illustrates Scenario 2 when non-optimal speed values are used for
N = 1 and N = 2. The shaded areas in the figures relate to a region where a
detection should be made.
6 Conclusions and Future Work
129
As can be noticed, the wear estimates are considerably affected by changes in
temperature when these speed values are used. However, when these two measurements are used together, the estimate improves significantly. The inclusion of
measurements around the optimal speed values should also increase robustness
to uncertainties in the wear model.
6 Conclusions and Future Work
A model-based maximum likelihood wear estimator was proposed based on a
known friction model and constant-speed friction data collected from experiments. Because friction is considerably affected by other factors than wear, in
particular temperature, a friction model that can describe these effects was suggested. Experiment design was considered to support the choice of speed levels
for the friction data which reveals more information about wear. Simulations and
case studies based on real data were considered to evaluate the approach. The
wear estimates achieved in the studies showed a clear response to wear-related
effects to friction, indicating that the approach may open up for condition based
maintenance of industrial robots.
The studies presented here are restricted to one type of robot/gearbox and in an
experimental verification performed in a lab. To verify the applicability of the
proposed solutions in an industrial scenario, a more extensive experimental campaign is needed. Also interesting is to consider other types of variations and how
they can affect the models and framework presented. For example, a change of
lubricant may require the re-estimation of all or some of the friction parameters.
It should be stressed that different characteristics of the problem are expected for
different devices, gearboxes and fault mechanisms. The results and discussion
presented here may however provide useful guidance for those interested in using similar approaches for different devices. Of key importance to the proposed
approach are the friction models used. The same ideas suggested for experiment
design and wear identification can in principle be extended to other devices and
model structures.
An extension to this work is to consider on-line wear estimation. This could perhaps be achieved by considering data from a friction observer, e.g. as presented
in Ray et al. (2001); Marton (2011). The sensitivity of such approach to unmodeled phenomena, e.g. due to dynamic friction and external disturbances, should
be considered carefully based on experiments performed on a real robot and different scenarios.
130
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Modeling and Identification of Wear in a Robot Joint
Bibliography
F. Al-Bender and J. Swevers. Characterization of friction force dynamics. IEEE
Control Systems Magazine, 28(6):64–81, 2008.
C. M. Bishop. Pattern Recognition and Machine Learning. Springer, New York,
USA, 1st edition, 2006.
A. C. Bittencourt and S. Gunnarsson. Static friction in a robot joint— Modeling
and identification of load and temperature effects. Journal of Dynamic Systems, Measurement, and Control, 134(5), July 2012.
A. C. Bittencourt, P. Axelsson, Y. Jung, and T. Brogårdh. Modeling and identification of wear in a robot joint under temperature disturbances. In Proceedings
of the 18th IFAC World Congress, volume 18, Milan, Italy, August 2011.
D. Brambilla, L. Capisani, A. Ferrara, and P. Pisu. Fault detection for robot manipulators via second-order sliding modes. IEEE Transactions on Industrial
Electronics, 55(11):3954–3963, November 2008.
F. Caccavale, P. Cilibrizzi, F. Pierri, and L. Villani. Actuators fault diagnosis for
robot manipulators with uncertain model. Control Engineering Practice, 17(1):
146–157, 2009.
W. Chen. Fault detection and isolation in nonlinear systems: observer and
energy-balance based approaches. Dissertation, Faculty of Eng. Automatic Control and Complex Systems, Duisubug-Essen University, October 2011.
S. E. Chick and M. B. Mendel. An Engineering basis for statistical lifetime models
with an application to tribology. IEEE Transactions on Reliability, 45(2):208–
215, 1996.
A. De Luca and R. Mattone. Actuator failure detection and isolation using generalized momenta. In Proceedings of the 2003 IEEE International Conference
on Robotics and Automation (ICRA), volume 1, pages 634–639, Taipei, Taiwan,
September 2003.
K. De Moerlooze, F. Al-Bender, and H. Van Brussel. A generalised asperity-based
friction model. Tribology Letters, 40:113–130, 2010.
W. E. Dixon, I. D. Walker, D. M. Dawson, and J. P. Hartranft. Fault detection
for robot manipulators with parametric uncertainty: A prediction-error-based
approach. IEEE Transactions on Robotics and Automation, 16(6):3628–3634,
2000.
I. Eski, S. Erkaya, S. Savas, and S. Yildirim. Fault detection on robot manipulators
using artificial neural networks. Robotics and Computer-Integrated Manufacturing, 27(1):115–123, July 2011.
V. Filaretov, M. Vukobratovic, and A. Zhirabok. Observer-based fault diagnosis
in manipulation robots. Mechatronics, 9(8):929–939, 1999.
Bibliography
131
B. Freyermuth. An approach to model based fault diagnosis of industrial robots.
In Proceedings of the 1991 IEEE International Conference on Robotics and Automation, volume 2, pages 1350–1356, April 1991.
S. C. Guo, M. H. Yang, Z. R. Xing, Y. Li, and J. Q. Qiu. Actuator fault detection
and isolation for robot manipulators with the adaptive observer. Advanced
Materials Research, 482 - 484(8):529–532, 2012.
P. Hamon, M. Gautier, and P. Garrec. Dynamic identification of robots with a
dry friction model depending on load and velocity. In Proceedings of the 2010
IEEE/RSJ International Conference on Intelligent Robots and Systems, pages
6187–6193, Taipei, Taiwan, October 2010.
N. Kammerer and P. Garrec. Dry friction modeling in dynamic identification for
robot manipulators: Theory and experiments. In Proceedings of the 2013 IEEE
International Conference on Mechatronics, pages 422–429, 2013.
K. Kato. Wear in relation to friction – a review. Wear, 241(2):151–157, 2000.
A. R. Lansdown, A. L. Price, and J. Larsen-Basse. Materials to resist wear – a
guide to their selection and use. Journal of Tribology, 109(2):379–380, 1987.
L. Marton. On-line lubricant health monitoring in robot actuators. In Proceedings of the 2011 Australian Control Conference (AUCC), pages 167–172, Melbourne, Australia, November 2011.
L. Marton. Energetic approach to deal with faults in robot actuators. In Proceedings of the 20th Mediterranean Conference on Control Automation (MED),
pages 85–90, Barcelona, Spain, July 2012.
L. Marton and F. van der Linden. Temperature dependent friction estimation:
Application to lubricant health monitoring. Mechatronics, 22(8):1078–1084,
2012.
M. McIntyre, W. Dixon, D. Dawson, and I. Walker. Fault identification for robot
manipulators. IEEE Transactions on Robotics, 21(5):1028–1034, October 2005.
E. Olsson, P. Funk, and M. Bengtsson. Fault diagnosis of industrial robots using
acoustic signals and case-based reasoning. In P. Funk and P. A. González Calero,
editors, Advances in Case-Based Reasoning, volume 3155 of Lecture Notes in
Computer Science, pages 13–15. Springer Berlin / Heidelberg, 2004.
B. Porat and B. Friedlander. Computation of the exact information matrix of
Gaussian time series with stationary random components. IEEE Transactions
on Acoustics, Speech and Signal Processing, 34(1):118–130, February 1986.
L. R. Ray, J. R. Townsend, and A. Ramasubramanian. Optimal filtering and
Bayesian detection for friction-based diagnostics in machines. ISA Transactions, 40(3):207–221, 2001.
A. T. Vemuri and M. M. Polycarpou. A methodology for fault diagnosis in robotic
systems using neural networks. Robotica, 22(04):419–438, 2004.
132
Paper B
Modeling and Identification of Wear in a Robot Joint
J. A. Williams. Wear and wear particles – some fundamentals. Tribology International, 38(10):863–870, 2005.
Paper C
A Data-Driven Approach to
Diagnostics of Repetitive Processes
in the Distribution Domain
Authors:
André Carvalho Bittencourt, Kari Saarinen, Shiva Sander-Tavallaey, Svante
Gunnarsson and Mikael Norrlöf .
Edited version of the paper:
A. C. Bittencourt, K. Saarinen, S. Sander-Tavallaey, S. Gunnarsson, and
M. Norrlöf. A data-driven approach to diagnostics of repetitive processes in the distribution domain – Applications to gearbox diagnostics
in industrial robots and rotating machines. Mechatronics, -(0):–, 2014.
Available online.
Parts of this paper were previously published in:
A. C. Bittencourt, K. Saarinen, and S. Sander-Tavallaey. A data-driven
method for monitoring systems that operate repetitively – applications
to wear monitoring in an industrial robot joint. In Proceedings of the
8th IFAC SAFEPROCESS, volume 8, Mexico City, Mexico, 2012.
A Data-Driven Approach to Diagnostics of
Repetitive Processes in the Distribution
Domain
André Carvalho Bittencourt∗ , Kari Saarinen∗∗ , Shiva Sander-Tavallaey∗∗ , Svante
Gunnarsson∗ and Mikael Norrlöf∗ ∗∗
∗ Dept.
∗∗ ABB
of Electrical Engineering,
Linköping University,
SE–581 83 Linköping, Sweden
AB
Västerås, Sweden
Abstract
This paper presents a data-driven approach to diagnostics of systems
that operate in a repetitive manner. Considering that data batches
collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing
the monitored data against an available nominal batch. The method
proposed considers the comparison of data in the distribution domain,
which reveals information of the data amplitude. This is achieved
with the use of kernel density estimates and the Kullback-Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement
and can be used without process interruption, in a batch manner. The
approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot joint and
for the diagnostics of gear faults in a rotating machine.
1 Introduction
In the manufacturing industry, preventive scheduled maintenance is a common
approach used to improve equipment’s safety, reliability and availability. This
setup delivers high availability, reducing operational costs (e.g., small downtimes)
with the drawback of high maintenance costs since unnecessary maintenance actions might take place. Condition based maintenance (cbm), “maintenance when
required”, can deliver a good compromise between operational and maintenance
costs, reducing the overall cost of service. The extra challenge of cbm is to define methods to determine the condition of the equipment. This can be done by
comparing the observed against the expected (known) behaviors of the system
through an algorithm. The output of such algorithm is a test quantity, i.e., a
135
136
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Data-Driven Diagnostics of Repetitive Processes
quantity that can be tested to determine the current faulti state of the system
(e.g., healthy/broken).
A common approach to generate test quantities is based on the use of residuals,
i.e., test quantities that are achieved based on deviations between measurements
and the output of a system model, see, e.g., Isermann (2011); Li and Zhou (2009).
A system model is a map from input to output data and provides important information about the behavior of the system, facilitating the generation of test
quantities. Different approaches for residual generation are based on, e.g., observers, parity-space and parameter identification. When a model of the system
is not available or it is too costly to be developed, alternatives are still possible.
These alternatives will typically require extra (redundant) sensory information
or expert knowledge about the measured data, e.g., their nominal frequency content or the use of labeled data. Essentially, however, any method will attempt to
generate quantities that can be used to infer the actual condition of the system
given the available knowledge and observations, i.e., data.
The use of model-based approaches is common for the diagnostics of machines.
In robotics, many approaches have been suggested based on the use of nonlinear observers, where the observer stability is typically guaranteed by analysis of
the decay rate of a candidate Lyapunov function, see e.g. Filaretov et al. (1999);
McIntyre et al. (2005); Caccavale et al. (2009); Guo et al. (2012); Brambilla et al.
(2008); De Luca and Mattone (2003); Dixon et al. (2000). Observers can also be
designed based only on data, without a first principles description of the system.
Data-driven design of observers is typically based on subspace identification of
linear models and have been suggested for fault detection by Ding et al. (2011);
Wang et al. (2011); Dong et al. (2012); Yin et al. (2013). Parameter estimation is
also a natural approach to model-based diagnostics because of the physical interpretation of the system parameters, see e.g. Bittencourt and Axelsson (2014);
Freyermuth (1991); Marton and van der Linden (2012).
In cases where the data are ordered in time, signal-driven methods are common
for machinery diagnostics. These are typically based on the use of integral transforms, e.g., Fourier, Radon, Karhunen-Loève or Wavelet. Each transform will
enhance different properties in the transformed domain and are suitable depending on the characteristics of the signal, e.g., periodic, stationary, etc. The analysis
of data in the frequency or time-frequency domains has found particular success for the monitoring of rotating machines, see, e.g., Taylor (1994); de Silva
(2007); Fan and Zuo (2006); Halim et al. (2008); Sander-Tavallaey and Saarinen
(2009); Isermann (2006, 2011). Some approaches have also been proposed for the
diagnostics of industrial robots with the use of additional sensory information
Olsson et al. (2004); Eski et al. (2011).
A common challenge to data-driven methods is that the characteristics of the
data will vary depending on the operating points, which may complicate the
determination of a fault presence. This is particularly restricting for an indusi A fault is defined as a deviation of at least one characteristic property of the system from the
acceptable/usual/nominal condition.
2 Data-Driven Diagnostics and Repetitive Systems
137
trial robot where the kinematic configuration of the robot may give varying load
torques at the joints during motion. This shortcoming can be circumvented by
considering data from a specific operation of the system, e.g., under repetition.
A repetitive operation is found in various applications, e.g., in automated manufacturing. Repetition can also be forced with the execution of specific diagnostic
routines but with the drawback of reduced availability. Much attention has been
given recently to repetitive processes (Rogers et al., 2013a,b). Study of repetitive
processes have mainly focused on control (Rogers et al., 2007; Sulikowski et al.,
2004) and estimation problems (Aguilar-Lopez and Martinez-Guerra, 2007). Some
approaches have been also suggested for model-based diagnostics, e.g., proposed
by Wu et al. (2011).
In this paper, a data-driven approach is proposed for the diagnostics of systems
that operate in a repetitive manner. It is considered that in case the condition of
the system is nominal, data batches collected from a repetitive behavior of the
system will be similar to each other and will differ if the condition changes. The
comparison of a given data batch against a nominal one can thus be used to infer
whether an abnormality is present. The test quantity proposed here relates to
changes in the distribution of these batches of data. This is made possible with
the use of kernel density estimators and the Kullback-Leibler distance between
distributions. A distribution domain approach does not consider the dynamics of
the system generating the data as is the case in, e.g., observer-based approaches.
Nevertheless, as it will be presented, this leads to very simple diagnostic solutions
that can perform well in practical setups.
The proposed framework was initially developed with the interest focused on the
diagnostics of wear in industrial robots and a preliminary version of the work can
be found in Bittencourt et al. (2012). Here, more aspects are covered, including
approaches to detection, isolation and reduction of sensitivity to disturbances.
More experimental and simulation results are also presented for the robotics application. An additional application is included for the diagnostics of rotating
machinery based on vibration data collected from an accelerometer. The paper is
organized as follows; a general presentation of data-driven diagnostics and repetitive systems is given in Section 2, followed by the presentation of the proposed
approach for diagnostics in the distribution domain in Section 3. The applications are presented in Sections 4 and 5. Conclusions and future work are given
in Section 6.
2 Data-Driven Diagnostics and Repetitive Systems
Consider a general system from which it is possible to extract a sequence of data
batches,
YK = [y 1 , · · · , y k , · · · , y K ],
(1)
where y k = [yk,1 , · · · , yk,n , · · · , yk,N ]T denotes the kth data batch in RN (e.g. measurements, known inputs) with batch index k and element index n. The sequence
138
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Data-Driven Diagnostics of Repetitive Processes
y k could have been generated as the result of deterministic and stochastic inputs,
z k and vk respectively, where vk is unknown and z k can have known and unknown components. For example, the data generation mechanism could be modeled as
y k = h(z k , vk ),
(2)
where h( · ) is an unknown function. The random inputs vk are unknown, e.g.,
noise. The known components of z k include control inputs uk and references r k .
Unknown components of z k include disturbances d k and faults f k .
The objective is to define a data-driven framework for the generation of test quantities to determine the presence of a fault f k . Because a data-driven approach depends on availability of data, it is assumed that data generated under no fault is
available. Let Y 0 = {y k : f k = 0} denote the set of data batches that were generated
under no fault, the following assumption is made.
A-1 (Nominal data are available) A labeled sequence y 0 ∈ Y 0 is available.
The rationale is then to generate test quantities from the comparison of the nominal data y 0 (available from Assumption A-1) against the remaining sequences y k .
In order to generate test quantities for y k using the nominal data y 0 , two basic
questions arise:
Q-1 How to characterize a sequence y k ?
Q-2 How to compare the sequence y 0 against y k ?
The first question targets the issue of finding a data processing mechanism of y k ,
written in a general form as g(k) , g(y k ) : RN 7→ G with domain G, whose output
are data features that can enhance the ability to discriminate the presence of nonzero f k . Given the nominal data in the transformed domain, g 0 , g(y 0 ), test
quantities, q(k), can be achieved from the comparison between g 0 and g(k). This
is typically, but not necessarily, done with the use of a distance function and is
represented as
q(k) , d(g 0 , g(k)) : G×G 7→ R+0 .
(3)
Different distances are possible depending on the domain G. For example, for
diagnostics of rotating machines g(k) could be the spectra of y k and d( · , · ) a
spectral distance, see, e.g., Basseville (1989) for more on distances between spectra.
2.1 Detection, performance and isolation
Let Qm,n = {q = d(g(i), g(j)) : y i ∈ Y m , y j ∈ Y n }, then the set Q0,0 describes the
behavior of the test quantity when no fault is present and Q0,f , where Y f = {y k :
f k , 0}, describes all possible faulty behaviors. A criterion for detectability of an
abnormality is that Q0,f is not completely contained in Q0,0 , i.e., Q0,f \Q0,0 , ∅.
Since the test quantity q(k) = d(g 0 , g(k)) measures how far g(k) is from the nomi-
2 Data-Driven Diagnostics and Repetitive Systems
139
nal g 0 , it is expected that it will remain close to zero if q(k) ∈ Q0,0 and to deviate
to positive values if q(k) ∈ Q0,f . Suppose that it is possible to find a threshold ~
such that q(k) ≤ ~ most of the times when q(k) ∈ Q0,0 , a simple criterion for detection is then to consider a threshold check. Let H0 denote the hypothesis that no
fault is present in y k , i.e., q(k) ∈ Q0,0 , and H1 denote the alternative hypothesis
that a fault is present, i.e., q(k) ∈ Q0,f , then the decision mechanism is
H1
(4)
q(k) ≷ ~
H0
and reads, decide for H0 if q(k) ≤ ~ otherwise decide for H1 .
The probabilities of error for this decision rule can be quantified given the probability distribution of the test quantity under the different hypotheses, denoted
p(q|H0 ) and p(q|H1 ). The probability of a false detection Pf , i.e., deciding for a
fault when none is present, and of correct detection Pd , i.e., deciding for a fault
when it is present can be evaluated by
Pf =
Z∞
0
p(q|H ) dq,
~
Pd =
Z∞
p(q|H1 ) dq.
(5)
~
Notice that for a fixed Pf there is an associated ~ (this is known as the NeymanPearson criterion for threshold selection (Van Trees, 2001)) and therefore a Pd .
For a satisfactory performance of the test quantity, low Pf and high Pd are typically desirable.
Since this is a data-driven framework, data from the different fault types are
needed a priori to address the isolation problem, i.e., determination of the fault
type present. Let Y m = {y k : f k = f m } denote the set of data generated under the
mth fault type with M possible fault modes, i.e., m ∈ M = {1, 2, · · · , M}. For fault
isolation it is assumed:
A-2 (Data from each fault type are available) A sequence y m ∈ Y m is available
for each mth fault type.
Once a fault is detected, the fault type can be determined by choosing y m that is
closest to y k in the sense of the transformation g( · ) and distance d( · , · ). That is,
decide for presence of the m∗ fault type according to
m∗ = arg min d(g m , g(k)).
m∈M
(6)
2.2 Repetitive systems
The amount of overlap between the sets Q0,0 and Q0,f relates to how difficult it
will be to determine the presence of a fault. Ultimately, no overlap is present, i.e.,
|Q0,0 ∩ Q0,f | = 0, when it is possible to determine the presence of a fault with no
errors. This is particularly difficult to achieve because y k , and thus Q0,0 and Q0,f ,
are affected by other inputs than faults. Since |Q0,0 ∩ Q0,f | ≤ min |Q0,0 |, |Q0,f |, an
140
Paper C
Data-Driven Diagnostics of Repetitive Processes
attempt to try to reduce the overlaps is to reduce the size of the sets themselves.
This can be achieved by restricting the behavior of the data y k or, conversely,
by restricting the possible input space. This paper focuses on monitoring data
collected from a repetitive operation of the system. Such data is achieved by a
system that is commanded to execute the same trajectory ℧ for each batch of monitored data. For a system operating in closed loop, this type of data is represented
by the set
Yr = {y k : r k = ℧}.
(7)
Alternatively, for open-loop systems the definition Yr = {y k : uk = ℧} applies.
Monitoring data y k ∈ Yr simplifies the problem since the behavior of the data will
be more predictable. Examples of systems that behave repetitively are common
in automation applications. A repetitive operation can also be forced in case the
input commands can be chosen freely; for instance data can be collected based on
the execution of scheduled diagnostic tests. This setup is also commonly found
in vibration analysis and in signal-based diagnostics, where data are collected
under particular operating conditions, e.g., of speed, load and acceleration.
Considering that faults are detectable for a system operating under repetition,
0,f
0
0
there may still be overlaps between the sets Q0,0
r and Q r defined by Y r = Y ∩ Y r
f
and Yr = Y f ∩ Yr . The presence of disturbances d k and noise vk may be important causes of overlaps. By collecting data in a controllable manner, it might be
possible to ensure repetition of the disturbance term, i.e., to have d k = d for all
batches. This is however too restrictive in many applications. Even if d k = d is
possible, the noise components will always affect the data. To broaden the scope
of the framework, a clear determination of the fault presence despite variations
of d k and vk is desirable, leading to the question:
Q-3 How to handle non-repetitive disturbances d k and noise vk ?
Questions Q-1 to Q-3 are addressed in the next section which defines the suggested approach for diagnostics of repetitive systems in the distribution domain.
3 A Distribution Domain Approach
3.1 Characterizing the data – Kernel Density Estimate
The alternative pursued in this work is to consider the distribution of y k , which
contains information about the amplitude behavior of the data. Even though information contained in the ordering may be lost, this is a valid approach since the
effects of faults often appear as changes in amplitude. Since data batches from a
repetitive operation are considered, i.e., y k ∈ Yr , it is expected that the data distribution will remain similar in case no fault is present. Because the mechanisms
that generated the data are considered unknown, the use of a nonparametric estimate of the distribution of y k is a suitable alternative. A nonparametric estimate
of the distribution p(y) based on the data vector y can be achieved from the empirical characteristic function. For a scalar random variable with probability den-
3 A Distribution Domain Approach
141
sity function p(y), the characteristic function ϑ(ν) : R → C is defined as (Durrett,
2010):
ϑ(ν) = E [e
νy
]=
Z∞
−∞
e νy p(y) dy = F−1 {p(y)}2π,
(8)
where F−1 { · } is the inverse Fourier transform. So the density function can be
found from the characteristic function through its Fourier transform. Following
Parzen (1962), given the sample y = [y1 , · · · , yN ]T , the empirical estimate of ϑ(ν)
is given by
N
X
b = 1
ϑ(ν)
e νyn ,
N
(9)
n=1
b
the objective is then to estimate the density function from ϑ(ν).
This is essentially
a spectrum estimation problem. A direct estimation of the density function from
b will however lead to an estimate with variance that
the Fourier transform of ϑ(ν)
does not decrease with N (Ljung, 1999, Section 6.4). To avoid this problem, the
empirical estimate of the characteristic function is multiplied with a weighting
function ψh (ν) = ψ(hν). The weighting function is typically symmetric, satisfying
ψ(0) = 1 and tends to zero when ν tends to infinity. The density estimate is then
given by
b
p (y) =
o
1
1 nb
F ϑ(ν)ψ(hν) =
2π
2π
1
=
2π
=
Z∞
1
N
−∞
N
X
1
Nh
n=1
κ
N
X
e
ν(yn −y)
n=1
Z∞
−∞
b
e −νy ϑ(ν)ψ(hν)
dν
N
1 X 1
ψ(hν) dν =
Nh
2π
n=1
yn − y
1
=
h
N
N
X
n=1
Z∞
e ν (
yn −y
h
) ψ(hν) d(hν)
−∞
κh (y − yn ),
where κh (y)h = F−1 {ψh (ν)}. The resulting estimate is known as a kernel density estimate (kde) and can also be generalized to the multidimensional case (Cacoullos,
1966). For a given sequence y k the resulting kde estimate is denoted b
p k (y) and
from the notation of Section 2, g(k) = b
p k (y), i.e., the kde is the data feature.
The function κh (y) is a kernel function, satisfying κh ( · ) ≥ 0 and that integrates to
one. Typical kernel functions and their Fourier transforms are shown in Figure 1.
The bandwidth parameter h controls the smoothness of the resulting estimate,
increasing the smoothness for larger values of h. When h → 0, the kernel function approaches a Dirac delta and the resulting estimate will be a set of impulses
located at the data points. In this work, a Gaussian kernel is used with h optimized for Gaussian distributions as described by Bowman and Azzalini (1997).
For a detailed treatment of kernel density estimators and criteria/methods for
choosing h see Parzen (1962); Cacoullos (1966); Bowman and Azzalini (1997);
Paper C
1.2 h = 1
1 h=2
0.8 h = 3
0.6
0.4
0.2
0
−0.2
−20−10 0 10 20
ν
1 h=1
=2
0.8 h
h=3
0.6
0.4
0.2
0
−5
0
y
1.2 h = 1
1 h=2
0.8 h = 3
0.6
0.4
0.2
0
−0.2
−20−10 0 10 20
ν
1.2 h = 1
1 h=2
h=3
0.8
0.6
0.4
0.2
0
−10 −5 0
ν
0.4
ψh (ν)
κh (y)
0.2
0.1
5
(b) Triangular.
ψh (ν)
(a) Uniform.
h=1
h=2
h=3
0.3
κh (y)
ψh (ν)
replacements
Data-Driven Diagnostics of Repetitive Processes
0
−5
0
y
5
(c) Gaussian.
1.2 h = 1
1 h=2
h=3
0.8
0.6
0.4
0.2
0
−5
0
5 10
ν
ψh (ν)
142
5
Figure 1: Kernel functions (upper row) and their respective Fourier transforms (bottom row).
Jones and Henderson (2009).
3.2 Comparing sequences – Kullback-Leibler distance
In statistics and information theory, the Kullback-Leibler divergence (kld) is
commonly used as a measure of difference between two probability distributions. For two continuous distributions on y, p A (y) and p B (y), it is defined as
(Reid and Williamson, 2011)
A
DKL p ||p
B
,
Z∞
−∞
p A (y) log
p A (y)
dy
p B (y)
(10)
The kld satisfies DKL p A ||p B ≥ 0 (Gibbs inequality), with equality if and only
if p A (y) = p B (y). The kld is not a distance as it is not symmetric in general. The
quantity
KL p A , p B , DKL p A ||p B + DKL p B ||p A ,
(11)
is however symmetric and is known as the symmetric Kullback-Leibler divergence or Kullback-Leibler distance. An answer to Question Q-2 can therefore
be given with the use of the KL distance defined in (11). From Assumption A-1,
fault-free data are always available, so that y 0 is known and b
p 0 (y) can be com
puted. The KL distance can thus be used as test quantities, i.e., q(k) = KL b
p 0, b
pk ,
3 A Distribution Domain Approach
143
remaining close to 0 in case b
p 0 (y) is close to b
p k (y) and otherwise deviating to positive values.
3.3 Handling non-repetitive disturbances and noise – data
weighting
One approach to address Question Q-3 is to weight the raw data y k according to
prior knowledge of the effects of faults, disturbances and noise in order to give
more relevance to parts of the data that relate to a fault. The approach considered
here will assume availability of a labeled dataset, where the fault status (present
or not) is known to each batch y and is the same to each of its elements yn . The
disturbance and noise components should contain variations that are expected to
be found during the system’s operation.
The labeled dataset is given by
f
YK , YK00 , YKf = y 01 , · · · ,
y 0K0 ,
f
y1 ,
··· ,
f
y Kf ,
(12)
f
with K0 fault-free data, y 0 ∈ Yr0 , and Kf = K − K0 faulty data y f ∈ Yr . Each batch
y k is weighted as
yk , w ⊙ y k ,
(13)
where ⊙ denotes the Hadamard product (element-wise multiplication). This yields
the weighted dataset
0
f
f
f
(14)
Y K , Y K0 , Y Kf = y01 , · · · , y0K0 , y1 , · · · , yKf .
The objective is to choose w to maximize the sensitivity to faults while decreasing
sensitivity to disturbances and noise.
In this work, simple criteria are considered in a compromise to explicit solutions.
As it will be shown, the results are related to linear discriminant analysis (lda)
used for dimensionality reduction and classification problems, see, e.g., Bishop
(2006). In lda, the inner product w T y is used instead of the Hadamard product and the objective is to reduce the dimension of the data. While the data
dimension is reduced in lda, the use of the Hadamard product keeps the data dimensionality and therefore the kde can still be computed, yielding the estimates
b
pk .
Notice that once the weights are chosen, the same vector w is used for new data
batches. For consistency, it is thus required that the data sequences are synchronized. This can however be overcome in case the weights are strongly correlated
to measured data. In such case, an approximate function can be used to describe
the weights relation to the data, e.g., described as a static function w( · ). The use
of such representation of the weights is illustrated in Section 4.2.
Paper C
144
Data-Driven Diagnostics of Repetitive Processes
Choosing w – Linear Discriminant Analysis
A criterion is to choose w that maximizes the distance between the averages of
0
f
the weighted datasets Y K0 and Y Kf . The average weighted vector for YK00 is


K0
K0
 1 X

1 X
0
0
0

µ =
w ⊙ y k = w ⊙ 
y k  = w ⊙ µ0
K0
K0
k=1
k=1
| {z }
, µ0
(15)
and similarly µf = w ⊙ µf . Based on the resulting average vectors, a natural criterion would be to find their kernel density estimates and choose w that maximizes
the KL distance between them. A general solution to this problem is not possible
since it depends on how the kde is computed (e.g. the kernel function chosen)
and optimization over (11). Instead, a simpler criterion is considered, where w is
chosen to maximize the difference between the means of µf and µ0 , i.e.,
N
N
1
1 X
1 X
f
wn µ n −
wn µ0n = w T µf − µ0 .
N
N
N
n=1
(16)
n=1
Constraining w to unit length w T w = 1 (otherwise the criterion can be made arbitrarily large), it is possible to find that (16) is maximized for (see e.g. (Bishop,
2006, Exercise 4.4)),
w∗ ∝ (µf − µ0 ).
(17)
The criterion (16) does not account for the variability found within each dataset,
e.g., caused by disturbances and noise. An alternative is to consider maximum
separation between the means of µ0 and µf while giving small variability within
0
each dataset and thus avoiding overlaps. The sample covariance for Y K0 (and
f
similarly for Y Kf ) is given by
0
Σ =
=
K0 T
1 X
w ⊙ y k − w ⊙ µ0 w ⊙ y k − w ⊙ µ0
K0
1
K0
k=1
K0 X
k=1
T
w ⊙ y k − µ0 w ⊙ y k − µ0
K0 T
1 X
= (ww ) ⊙
y k − µ0 y k − µ0 = (ww T ) ⊙ Σ0 .
K0
k=1
|
{z
}
T
,Σ0
The total covariance is thus Σ , (wwT ) ⊙ (Σf + Σ0 ). Considering the trace of Σ as
a measure for the variability of the weighted data, an alternative criterion to (16)
4 Wear Monitoring in an Industrial Robot Joint
145
is to consider maximization of the ratio
2
T
T
w T µ f − µ0
w T µ f − µ0 µ f − µ0 w w T µ f − µ0 µ f − µ0 w
=
.
= P
N
2
f
0
tr Σ
w T I ⊙ Σ f + Σ0 w
n=1 wn [Σ ]nn + [Σ ]nn
This is a special case of the Fisher criterion in lda. It can be shown, see e.g. (Bishop,
2006, Section 4.1.4), that the solution for this problem is given by
−1
w ∗ = I ⊙ Σ f + Σ0
(µf − µ0 ).
(18)
That is, each weight wn∗ is proportional to the ratio between the average changes
f
caused by faults, µn − µ0n , and the total variability found in the data, [Σ0 ]nn +
f
[Σ ]nn , caused by disturbances and noise.
4 Wear Monitoring in an Industrial Robot Joint
In this first application, the objective is to determine the presence of excessive levels of wear, ̟, in the gearbox of a robot joint. Because increased wear levels may
lead to increased friction in the gearbox, it is possible to monitor friction to infer about wear. Since the friction torques must be overcome by the applied motor
torques, τ, during its operation, it is possible to extract information about friction
from available data. Friction is however not only affected by wear, but also by unknown disturbances, such as variations of load torques, τ ℓ , and the lubricant
temperature, ξ, see Figure 2i . While it may be simpler to ensure constant load
conditions, temperature is the result of complicated losses mechanisms in the
joint and heat exchanges with the environment which are difficult to control. The
effects of τ ℓ and ξ to friction are in fact comparable to those caused by ̟ (recall
Figure 2) and the problem is therefore challenging. In Bittencourt and Axelsson
(2014), a model-based approach was suggested for this problem based on the
identification of a wear parameter from friction data collected under dedicated
experiments. A shortcoming with this approach lies in the need of a detailed friction model, which requires a considerable amount of experiments to be found.
The data-driven approach suggested here is considered as an alternative which
requires little design effort since no model development is needed.
Since τ is affected by friction, and thus by wear, torque (currentii ) data are considered for the generation of test quantities. The monitored data are collected from
repetitive executions of a trajectory ℧. Relating to the notation introduced in Section 2, the deterministic unknown input of interest, f , is the wear level ̟ and
the monitored data, y, is τ which is affected by disturbances, d, caused by load τ ℓ
and temperature ξ and by measurement noise v. A trajectory, ℧, is a known deteri Throughout the paper, all torque quantities are normalized to the maximum allowed torque and
are therefore dimensionless.
ii In the application, a torque estimate based on a constant relationship between current measurements is used. This simplification is commonly used for control purposes since the current controller
has much faster dynamics compared to the dynamics of the robot arm and such estimate is therefore
perceived as the control input signal.
0.2
ξ
ξ
ξ
ξ
τf
0.15
0.1
Data-Driven Diagnostics of Repetitive Processes
= 33, τ ℓ = 0.70
= 80, τ ℓ = 0.70
= 33, τ ℓ = 0.01
= 80, τ ℓ = 0.01
0.05
0
0
offset: 0.038
100
200
300
ϕ̇ [rad/s]
(a) Effects of load and temperature.
0.1
0.08
0.06
0.04
0.02
0
τf
Paper C
146
0
offset: 0.017
50 100 150 200 250
ϕ̇ [rad/s]
(b) Effects of wear.
Figure 2: Friction levels τ f in a robot joint as a function of motor speed ϕ̇
under different conditions of wear, temperature and load. The offset values
were removed for a comparison, their values are shown by the dotted lines.
The data were collected from similar gearboxes and are directly comparable.
Notice the different scales used and the larger amplitude of effects caused
by temperature and load compared to those caused by wear. In (b), the colormap relates to the length of accelerated wear tests during which the curves
were registered.
ministic sequence used as a reference to the motion control, i.e., it relates to r. In
many applications, the same trajectory is executed over and over again, ensuring
a repetitive behavior of the robot. For the results presented here, data collected
from the execution of a trajectory ℧ based on a test-cycle are used. Torque data
collected form this trajectory can be seen in Figure 3a. Nominal torque data, τ0 ,
are achieved from the execution of ℧ when the gearbox is new and no significant
wear is present.
Section 4.1 presents experimental results for the wear monitoring problem when
the changes in disturbances are kept small. In Section 4.2, temperature disturbances are introduced in simulation studies and the use of the weights described
in Section 3.3 is illustrated to reduce sensitivity to disturbances.
4.1 Experimental studies under constant disturbances
Accelerated wear tests were performed with ABB IRB 6620 industrial robots
with the objective of studying the wear effects. In an accelerated wear test, the
robot is run under high load and stress levels for several months or years until the wear levels become significant and maintenance is required. Throughout
the tests, the trajectory ℧ was executed regularly a total of K times yielding a
dataset [τ0 , · · · , τ K−1 ]. The data were collected from axis two of the robot which
is equipped with a rotary vector gearbox type. The experiments were performed
in a lab, in a setup to avoid temperature variationsi and no load variations were
present. It is thus considered that the disturbances had a repetitive behavior, i.e.,
i The environment temperature was controlled and the experiments were only performed after the
robot temperature was expected to be in equilibrium with the environment.
4 Wear Monitoring in an Industrial Robot Joint
147
d k = d over all batches. The data batch taken from the start of the operation
of a gearbox is considered to be fault-free and is labeled as nominal, τ0 . The
test quantities q(k) = KL b
p 0, b
p k are computed for k = 1, . . . , K − 1 and used to
indicate presence of faults. Data collected from two accelerated wear tests are
considered here. For an illustration of the wear behavior during the experiments,
the friction levels of the joint were estimated using a dedicated experiment (see
Bittencourt and Gunnarsson (2012) for a description of such experiment) at each
kth execution of ℧ and are shown as function of motor speed ϕ̇.
For the first case, displayed in Figure 3, K = 36 batches of data are considered.
From analysis of the friction levels in Figure 3c, it is possible to note that wear
only starts to considerably affect friction after k = 25. The effects of wear to the
torque sequences, shown in Figure 3a, appear as small variations in amplitude
due to increased friction. The variations in the torque sequences are more easily distinguishable in the distribution domain as seen in Figure 3b. Wear affects
the location and size of the peaks of the distributions. Notice further that the
distributions are similar for k ≤ 25 when the robot condition has not significantly changed. The resulting test quantity, shown in Figure 3d, shows a clear
response to the changes in friction, remaining close to 0 for k ≤ 25 and increasing thereafter. To allow for cbm, it is considered that, in this test, a fault should
be detected before k = 30. Using data for k ≤ 25, the mean and standard deviation for the (considered) nominal behavior of the test quantity are estimated
as [µ0 , σ0 ] = [1.19 10−2 , 5.09 10−3 ]. The dashed line in Figure 3d shows the value of
~ = µ0 + 3σ0 which could be used as a threshold.
The second case, shown in Figure 4, illustrates the situation where a gearbox is
replaced after a wear related failure takes place. A total of K = 111 data batches
are collected during accelerated wear tests using the same test-cycle. A gearbox
failure occurs at k = 73 when it is replaced by a new one. The friction curves
related to the faulty gearbox are shown in Figure 4c, where can be noticed that
the changes due to wear start to appear around k = 64. The related distribution
estimates for this gearbox are shown in Figure 4a where a similar behavior as
in the previous case can be noticed, with changes in the size and position of the
distributions’ peaks. The data densities for the replaced gearbox can be seen in
Figure 4b where it is possible to notice that no significant variations are present.
The test quantity is shown in Figure 4d, where, as in the previous case, ~ = µ0 +3σ0 .
The filled circle highlights the moment when the gearbox was replaced. As can
be seen in these studies, an early detection of the increased wear is made possible
with the use of the proposed test quantity, allowing for cbm.
4.2 Simulation studies under temperature disturbances
Simulation studies were carried out to illustrate the ideas to reduce sensitivity to
disturbances and noise presented in Section 3.3. The use of simulations allow for
more detailed studies of the effects of the disturbances compared to what could
be achieved based on experiments in a feasible manner. The simulation model is
based on the two link manipulator with elastic gear transmission presented in the
0.02
τ
0.01
0
0
2
4
6
8
t [sec]
Data-Driven Diagnostics of Repetitive Processes
30
25
20
15
10
5
10
35
8 30
6 25
4 20
15
2 10
0 5
−0.5
b
p (y)
Paper C
148
(a) Monitored torque data.
0.04
0.02
0
20
60
40
ϕ̇ [rad/s]
(c) Friction curves.
q(k)
τf
0.06
τ
1
0.5
(b) Estimated distributions.
35
30
25
20
15
10
5
0.08
0
80
1
0.8
0.6
0.4
0.2
0
0
10
20
k
30
(d) Test quantity, q(k) = KL b
p 0, b
pk .
Figure 3: Monitoring of a wear fault in an industrial robot joint under accelerated wear tests and controlled load and temperature disturbances. The
colormaps relate to the experiment length k . The friction changes caused by
wear were estimated during the experiments and are shown in (c) for a comparison. The monitored torque data from the execution of the trajectory ℧
are shown in (a), their respective KDEs were computed using a Gaussian kernel and are shown in (b). At k = 0, it is considered
that the robot is fault-free
and the test quantity given by q(k) = KL b
p 0, b
p k is shown in (d) where the
dashed line represents an upper limit for its nominal behavior. Notice the
clear response of the test quantity to the wear changes.
4 Wear Monitoring in an Industrial Robot Joint
8
0
b
p (τ)
b
p (τ)
20
2
0
−0.5
τ
0.5
0
1
60
20
60
40
ϕ̇ [rad/s]
(c) Friction curves for 0 ≤ k ≤ 72.
−0.5
0
τ
0.5
1
1
40
20
80
(b) Estimated distributions for 73 ≤ k ≤
110.
q(k)
τf
0
90
2
(a) Estimated distributions for 0 ≤ k ≤
72.
0.1
0.08
0.06
0.04
100
4
40
4
110
6
60
6
149
80
0.5
0
0
20
40
60
k
80 100
(d) Test quantity, q(k) = KL b
p 0, b
pk .
Figure 4: Monitoring of a wear fault in an industrial robot joint under accelerated wear tests and controlled load and temperature disturbances. Data
collected from the same trajectory ℧ used in Figure 3 are considered. A wear
fault develops in the gearbox from k = 0 to k = 72, whereafter the faulty
gearbox is replaced by a new one. The data distribution estimates for the
faulty gearbox are shown in (a), which present similar behaviors as for the
previous case, recall Figure 3b; the respective friction curves are shown in
(c). The data distributions for the new gearbox are shown in (b), where only
small deviations are visible. The nominal data are assigned at the start of operation for the gearboxes at k = 0 and at k = 73. The resulting test quantities
are shown in Figure (d), with a clear response to the friction changes and a
regular behavior when no fault is present; the circle in the figure highlights
when the gearbox replacement took place and the dashed lines represent an
upper limit for the nominal behavior of the test quantity.
150
Paper C
Data-Driven Diagnostics of Repetitive Processes
benchmark problem of Moberg et al. (2008). With the objective of studying friction changes related to wear in a robot joint, the static friction model described
in Bittencourt and Axelsson (2014) is included in the simulation model. The friction model included was developed from empirical studies in a robot joint and
describes the effects of angular speed ϕ̇, manipulated load torque τ ℓ , temperature ξ, and wear ̟.
Finding the weights w
According to the procedures described in Section 3.3, a labeled dataset is needed
in order to find the optimal weights. The dataset is achieved here based on simulations of the same test-cycle trajectory ℧ used in Section 4.1. Each labeled dataset
f
YK00 and YKf contains K0 = Kf = 100 batches with torque data generated from
n
o
Yr0 = τ k : ̟k = 0, ξ k = ξ (k) , ℧k = ℧
(19a)
n
o
f
Yr = τ k : ̟k = ̟c , ξ k = ξ (k) , ℧k = ℧
(19b)
respectively, where ̟c = 35 is a wear level considered critical to generate an alarm
(see Bittencourt and Axelsson (2014) for details of the wear model). Here, ξ k is
considered constant for each kth run with value ξ (k) determined randomly from
a uniform distribution
h
i
ξ (k) ∼ U ξ, ξ + ∆ξ
(19c)
with lower level given by ξ = 30◦ C and width ∆ξ = 40◦ C. This assumption is
carried out for analysis purposes and allows for great variations of temperature.
The optimal weights given in (17) and (18) depend on the average changes found
f
in the data, µn − µ0n , and the total variability, [Σ0 ]nn + [Σf ]nn . These quantities
are computed based on the labeled dataset and are displayed in Figure 5a as a
function of the motor speed ϕ̇. As can be seen, the optimal weights present a
strong correlation with ϕ̇. This is not a surprise since the effects of ̟ and ξ
depend on ϕ̇, recall Figure 2. In the same figure, worst case estimates along speed
f
are also shown (solid lines), giveb by µn − µ0n closest to zero and largest [Σ0 ]nn +
f
[Σ ]nn . Figure 5b presents the ratio for such worst case estimates, which are
considered as the optimal weights according to (18). The solid line in Figure 5b
is a function approximation of the optimal weights given by
w(ϕ̇) = sech(β ϕ̇) tanh(α ϕ̇)
(20)
with α = 1.45 10−2 and β = 4.55 10−2 . The parametrization of the weight vector as a
function of ϕ̇ allows for a more general use of the optimal weights since the same
weighting function can be used for other trajectories. The optimal weighting
function selects a speed region that is more relevant for wear monitoring, giving
more emphasis to data in low to intermediate speed regions. A similar behavior
was also found in Bittencourt and Axelsson (2014) for the achievable quality of a
wear estimate for different speeds.
5 Gearbox Monitoring based on Vibration Data
4
0
w
2
0.2
0.1
0
−0.1
−0.2
151
f
(µn − µ0n )
[Σ0 ]nn + [Σf ]nn
−200
0
ϕ̇ [rad/s]
(a) Average effects.
−200
0
ϕ̇ [rad/s]
200
(b) Optimal weights.
Figure 5: Choice of optimal weights w. The effects of disturbances by temperature and faults are shown in (a), together with worst case estimates
(black lines). The optimal weights for the worst case estimates are shown
in (b) together with a function approximation (solid). Notice how the optimal region for wear monitoring is concentrated in a narrow speed range.
Improvements in detection performance
To illustrate the possible improvements achieved with the use of the weighting
function, an abrupt change detection is considered. Given a nominal data batch
τ0 ∈ Yr0 , the detection problem is to decide whether a test batch τ k belongs to Yr0
f
f
or Yr based on the test quantity q(k) = KL b
p 0, b
p k and where the sets Yr0 and Yr
are given by (19). This corresponds to the following hypotheses
H0 : τ k ∈ Yr0 ,
f
H1 : τ k ∈ Y r
(21a)
where H0 indicates that no wear fault is present, with ̟ = 0, and H1 indicates
presence of a wear fault of size ̟c . For given values of ̟c , ξ and ∆ξ , the testcycle trajectory ℧ is simulated to generate data according to (19). The probability
densities of the test quantity under each hypothesis are estimated based on 5000
Monte Carlo runs with and without the use of the weighting function. Based on
the hypotheses densities’, the probability of detection Pd is computed according
to (5) for a threshold check when the probability of false alarm is Pf = 0.01.
Figure 6a presents the achieved Pd as a function of ∆ξ for the fixed ̟c = 35 and ξ =
30◦ C with and without the use of the weighting function. Notice that the use of
the weighting function considerably improves Pd under temperature variations,
but for too large ∆ξ it becomes difficult to distinguish the effects. A similar study
is performed to illustrate how ̟c affects the performance. Figure 6b presents Pd
as a function of ̟c for the fixed ∆ξ = 25◦ C and ξ = 30◦ C. The improvements
achieved using the weighted data are clear.
5 Gearbox Monitoring based on Vibration Data
In this application, vibration data collected from the gearbox test rig described
in Fan and Zuo (2006); Halim et al. (2008) are considered. The test rig is com-
Paper C
152
0.5
0
0
1
raw data
weighted data
Pd
Pd
1
Data-Driven Diagnostics of Repetitive Processes
10
20
30
∆ξ
40
(a) Temperature variations.
50
raw data
weighted data
0.5
0
10
20
30
̟c
40
50
(b) Fault size.
Figure 6: Probability of detection Pd when Pf = 0.01 for an abrupt fault with
̟c = 35 as a function of temperature variations ∆ξ (a) and as function of the
wear change size ̟c for ∆ξ = 25◦ C (b). Notice the considerable improvements
when using the weighted data.
posed of a motor coupled to a gearbox with three shafts and four spur gears. It
is possible to study the effects of different types of gear faults in the rig by replacing healthy gears with damaged ones. Four different behavioral modes are
considered:
•
•
•
•
m = 0:
m = 1:
m = 2:
m = 3:
healthy gears are used,
a gear at the input shaft is damaged,
a gear at the output shaft is damaged,
a gear at the input and a gear at the output shafts are damaged.
Fault detection approaches for this problem have been proposed in Fan and Zuo
(2006) with the use of Hilbert and Wavelet packet transform and in Halim et al.
(2008) with a combination of Wavelet transform and time domain averaging. In
these approaches, the data are transformed to a time-frequency domain where
different faults can be distinguished based on inspection of the transformed data.
The distribution domain approach proposed here is considered as an alternative
for diagnostics.
Data collected from an accelerometer placed close to the output shaft are considered for the analysis. All data collection was performed under constant load and
speed conditions in all settings, ensuring a repetitive behavior. For each different
condition, 8 × 1024 samples are available with data sampled at 2.56 KHz. The
datasets are divided in K = 8 batches with N = 1024 samples to form YKm for each
mode m. Figure 7a shows the fist data batch y 1m for each mode m, notice that
it is difficult to distinguish differences in the data sequences. In Figure 7b, the
density estimates b
p km (y) for every batch in every mode are displayed. Notice the
smaller variability of the distribution estimates within each mode compared to
the variability found between modes.
To evaluate the detection performance, q(k) = KL b
p 0, b
p k is computed for every
possible pair y 0 and y m from YK0 and YKm respectively for m ∈ {0, 1, 2, 3}. Since
the distances are computed in pairs, this gives 28 samples from the nominal set
5 Gearbox Monitoring based on Vibration Data
y [V]
0.05
153
m=0
m=1
m=2
m=3
0
−0.05
0
1000
2000
n
3000
4000
(a) Data sequences.
30
20
m=0
m=1
m=2
m=3
10
0
−0.04 −0.02
m=0
m=1
m=2
m=3
60
p(q|Hm )
p̂ km (y)
40
0 0.02 0.04
y [V]
(b) Density estimates.
40
20
0
0
0.1 0.2 0.3 0.4 0.5
q(k)
(c) Test quantity behavior.
Figure 7: Diagnostics of a gearbox based on vibration data. A total of four
different modes m are possible. An example of data sequence from each
mode is shown in (a). The data behavior in the distribution domain is shown
in (b). The statistical behavior of the test quantity given by KL b
p 0, b
p k is
shown for y k ∈ Hm in (c). Notice that, despite the densities in (b) being
similar, the test quantity clearly indicates the presence of a change in (c).
Paper C
154
Data-Driven Diagnostics of Repetitive Processes
Q0,0 (K = 8 combined two by two) and K 2 = 64 samples from the sets Q0,m for
m ∈ {1, 2, 3}. The hypotheses densities p(q|Hm ) are estimated based on these samples and are shown in Figure 7c. Notice the clear separation between the null
hypothesis density, p(q|H0 ), from the alternatives. For Pf = 0.01, Pd is computed
according to (5) for a threshold check when deciding between H0 and Hm for
m ∈ {1, 2, 3}; the achieved values are 1, 0.991 and 1 respectively. To illustrate
the approach to isolation as given in (6), the first data batch in each mode, y 1m , is
considered available. The isolation criterion given by
m∗ = arg
min
m∈{0,1,2,3}
KL (b
p1m , b
pk )
chooses the correct mode for all (K −1)×4 remaining data batches.
6 Conclusions and Future Work
The suggested framework considers the monitoring of changes in the distribution
of data batches. Because no prior knowledge is assumed about the data behavior,
nonparametric kernel density estimates are used which give great flexibility, are
simple to implement and have an inherent smoothing behavior. The validity
of the framework and methods were illustrated with promising results on real
case studies and simulations for gearbox monitoring in robotics and rotating machines. An important advantage of the framework presented is that no model or
expert knowledge of the system are required. Furthermore, it gives an alternative
for systems where faults affect the data amplitude but where stationary, periodic
or linear behaviors are difficult or not possible, as in the robotics application.
In case sets of labeled data batches are available rather than a single one, the generation of test quantities can be performed by considering the distance between a
test batch and the labeled sets. When the sets of labeled data batches contain data
from broad operations of the system, it is likely that using this simple extension
will result in test quantities that present better performance compared to those
generated based on a single labeled data batch.
The determination of a change in data collected under repetitive conditions simplifies the diagnostics problem considerably. However, it might not possible to
ensure the same repetitive behavior of the system. This is the case, for example, in
the industrial robotics application where trajectories are normally only repeated
trough a certain period, depending on a manufacturing plan. It is thus relevant
to study approaches to handle systems with a varying repetitive behavior. The
effects of different kernel functions for the kde, choice of bandwidth parameter
and use of different distances between densities are also important.
Acknowledgments
The authors would like to thank Professor Sirish L. Shah from the University of
Alberta, Canada, for sharing the data used in the studies of Section 5.
Bibliography
155
Bibliography
R. Aguilar-Lopez and R. Martinez-Guerra. Robust state estimation for repetitive
operating mode process: Application to sequencing batch reactors. Chemical
Engineering Journal, 126(2):155–161, 2007.
M. Basseville. Distance measures for signal processing and pattern recognition.
Signal Processing, 18(4):349–369, 1989.
C. M. Bishop. Pattern Recognition and Machine Learning. Springer, New York,
USA, 1st edition, 2006.
A. C. Bittencourt and P. Axelsson. Modeling and experiment design for identification of wear in a robot joint under load and temperature uncertainties based
on friction data. IEEE/ASME Transactions on Mechatronics, 19(5):1694–1706,
October 2014.
A. C. Bittencourt and S. Gunnarsson. Static friction in a robot joint— Modeling
and identification of load and temperature effects. Journal of Dynamic Systems, Measurement, and Control, 134(5), July 2012.
A. C. Bittencourt, K. Saarinen, and S. Sander-Tavallaey. A data-driven method for
monitoring systems that operate repetitively - applications to wear monitoring
in an industrial robot joint. In Proceedings of the 8th IFAC SAFEPROCESS,
volume 8, Mexico City, Mexico, 2012.
A. W. Bowman and A. Azzalini. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science
Series). Oxford University Press, USA, November 1997.
D. Brambilla, L. Capisani, A. Ferrara, and P. Pisu. Fault detection for robot manipulators via second-order sliding modes. IEEE Transactions on Industrial
Electronics, 55(11):3954–3963, November 2008.
F. Caccavale, P. Cilibrizzi, F. Pierri, and L. Villani. Actuators fault diagnosis for
robot manipulators with uncertain model. Control Engineering Practice, 17(1):
146–157, 2009.
T. Cacoullos. Estimation of a multivariate density. Annals of the Institute of
Statistical Mathematics, 18:179–189, 1966.
A. De Luca and R. Mattone. Actuator failure detection and isolation using generalized momenta. In Proceedings of the 2003 IEEE International Conference
on Robotics and Automation (ICRA), volume 1, pages 634–639, Taipei, Taiwan,
September 2003.
C. W. de Silva. Vibration Monitoring, Testing and Instrumentation. CRC Press,
April 2007.
S. X. Ding, S. Yin, Y. Wang, Y. Wang, Y. Yang, and B. Ni. Data-driven design of observers and its applications. In Proceedings of the 18th IFAC World Congress,
Milan, Italy, 2011.
156
Paper C
Data-Driven Diagnostics of Repetitive Processes
W. E. Dixon, I. D. Walker, D. M. Dawson, and J. P. Hartranft. Fault detection
for robot manipulators with parametric uncertainty: A prediction-error-based
approach. IEEE Transactions on Robotics and Automation, 16(6):3628–3634,
2000.
J. Dong, M. Verhaegen, and F. Gustafsson. Robust fault detection with statistical
uncertainty in identified parameters. IEEE Transactions on Signal Processing,
60(10):5064–5076, 2012.
R. Durrett. Probability: Theory and examples. Cambridge University Press, August 2010.
I. Eski, S. Erkaya, S. Savas, and S. Yildirim. Fault detection on robot manipulators
using artificial neural networks. Robotics and Computer-Integrated Manufacturing, 27(1):115–123, July 2011.
X. Fan and M. J. Zuo. Gearbox fault detection using Hilbert and Wavelet packet
transform. Mechanical Systems and Signal Processing, 20(4):966–982, 2006.
V. Filaretov, M. Vukobratovic, and A. Zhirabok. Observer-based fault diagnosis
in manipulation robots. Mechatronics, 9(8):929–939, 1999.
B. Freyermuth. An approach to model based fault diagnosis of industrial robots.
In Proceedings of the 1991 IEEE International Conference on Robotics and Automation, volume 2, pages 1350–1356, April 1991.
S. C. Guo, M. H. Yang, Z. R. Xing, Y. Li, and J. Q. Qiu. Actuator fault detection
and isolation for robot manipulators with the adaptive observer. Advanced
Materials Research, 482 - 484(8):529–532, 2012.
E. B. Halim, M. S. Choudhury, S. L. Shah, and M. J. Zuo. Time domain averaging
across all scales: A novel method for detection of gearbox faults. Mechanical
Systems and Signal Processing, 22(2):261–278, 2008.
R. Isermann. Fault-Diagnosis Systems - An Introduction from Fault Detection to
Fault Tolerance. Springer, 1st edition, 2006.
R. Isermann. Fault-diagnosis applications - model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems.
Springer, New York, 2011.
M. Jones and D. Henderson. Maximum likelihood kernel density estimation: On
the potential of convolution sieves. Computational Statistics & Data Analysis,
53(10):3726–3733, 2009.
X. Li and K. Zhou. A time domain approach to robust fault detection of linear
time-varying systems. Automatica, 45(1):94–102, 2009.
L. Ljung. System Identification: Theory for the User. Prentice Hall PTR, 2nd
edition, December 1999.
L. Marton and F. van der Linden. Temperature dependent friction estimation:
Bibliography
157
Application to lubricant health monitoring. Mechatronics, 22(8):1078–1084,
2012.
M. McIntyre, W. Dixon, D. Dawson, and I. Walker. Fault identification for robot
manipulators. IEEE Transactions on Robotics, 21(5):1028–1034, October 2005.
S. Moberg, J. Öhr, and S. Gunnarsson. A benchmark problem for robust control
of a multivariable nonlinear flexible manipulator. In Proceedings of the 17th
IFAC World Congress, Seoul, South Korea, March 2008.
E. Olsson, P. Funk, and N. Xiong. Fault diagnosis in industry using sensor readings and case-based reasoning. Journal of Intelligent & Fuzzy Systems, 15:
41–46, December 2004.
E. Parzen. On estimation of a probability density function and mode. The Annals
of Mathematical Statistics, 33(3):1065–1076, 1962.
M. D. Reid and R. C. Williamson. Information, divergence and risk for binary
experiments. Journal of Machine Learning Research, 12:731–817, 2011.
E. Rogers, K. Galkowski, and D. H. Owens. Control systems theory and applications for linear repetitive processes. Springer, 2007.
E. Rogers, K. Galkowski, and D. H. Owens. Two decades of research on linear
repetitive processes part i: Theory. In Proceedings of the 8th International
Workshop on Multidimensional Systems (nDS), Erlanger, Germany, 2013a.
E. Rogers, K. Galkowski, W. Paszke, and D. H. Owens. Two decades of research on
linear repetitive processes part ii: Applications. In Proceedings of the 8th International Workshop on Multidimensional Systems (nDS), Erlanger, Germany,
2013b.
S. Sander-Tavallaey and K. Saarinen. Backlash identification in transmission unit.
In Proceedings of the 2009 IEEE Control Applications & Intelligent Control,
pages 1325–1331, Singapore, Singapore, July 2009.
B. Sulikowski, K. Gałkowski, E. Rogers, and D. H. Owens. Output feedback
control of discrete linear repetitive processes. Automatica, 40(12):2167–2173,
2004.
J. I. Taylor. The Vibration Analysis Handbook. Vibration Consultants, February
1994.
H. L. Van Trees. Detection, Estimation and Modulation Theory, Part I. Wiley,
New York, 1st edition, 2001.
Y. Wang, G. Ma, S. X. Ding, and C. Li. Subspace aided data-driven design of
robust fault detection and isolation systems. Automatica, 47(11):2474–2480,
2011.
L. Wu, X. Su, and P. Shi. Mixed H2 /H∞ approach to fault detection of discrete
linear repetitive processes. Journal of the Franklin Institute, 348(2):393–414,
2011.
158
Paper C
Data-Driven Diagnostics of Repetitive Processes
S. Yin, S. X. Ding, A. H. Abandan Sari, and H. Hao. Data-driven monitoring for
stochastic systems and its application on batch process. International Journal
of Systems Science, 44(7):1366–1376, 2013.
Paper D
Simulation based Evaluation of Fault
Detection Algorithms with
Applications to Wear Diagnosis in
Manipulators
Authors:
Andreas Samuelsson, André Carvalho Bittencourt, Kari Saarinen, Shiva
Sander-Tavallaey, Mikael Norrlöf , Hans Andersson and Svante Gunnarsson.
Edited version of the paper:
A. Samuelsson, A. C. Bittencourt, K. Saarinen, H. Andersson, S. S. Tavallaey, M. Norrlöf, and S. Gunnarsson. Simulation based evaluation of
fault detection algorithms with applications to wear diagnosis in manipulators. In Proceedings of the 19th IFAC World Congress, Cape
Town, South Africa, 2014.
Simulation based Evaluation of Fault
Detection Algorithms with Applications to
Wear Diagnosis in Manipulators
Andreas Samuelsson∗∗ , André Carvalho Bittencourt∗ , Kari Saarinen∗∗ , Shiva
Sander-Tavallaey∗∗ , Mikael Norrlöf∗ ∗∗ , Hans Andersson∗∗ and
Svante Gunnarsson∗
∗ Dept.
∗∗ ABB
of Electrical Engineering,
Linköping University,
SE–581 83 Linköping, Sweden
AB,
Västerås, Sweden
Abstract
Fault detection algorithms (fdas) process data to generate a test quantity. Test quantities are used to determine the presence of faults in a
monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an fda, it is important to
evaluate it in scenarios of practical relevance. In this paper, simulation based approaches are proposed in an attempt to determine: i)
which disturbances affect the output of an fda the most; ii) how to
compare the performance of different fdas; and iii) which combinations of fault change size and disturbances variations are allowed to
achieve satisfactory performance. The ideas presented are inspired by
the literature of design of experiments, surrogate models, sensitivity
analysis and change detection. The approaches are illustrated for the
problem of wear diagnosis in manipulators where three fdas are considered. The application study reveals that disturbances caused by
variations in temperature and payload mass error affect the fdas the
most. It is also shown how the size of these disturbances delimits the
capacity of an fda to relate to wear changes. Further comparison of
the fdas reveal which performs “best” in average.
1 Introduction
Fault detection and fault diagnosis can be used to improve safety, reliability, availability, and maintainability of technical systems (Isermann, 2006). In fault detection, observations from the system, e.g., data, are processed and compared to
available knowledge of the system to generate symptoms. Symptoms are a partial
diagnosis of the system, i.e., a statement about which states of the system could
possibly explain the current observations. The diagnosis of complex systems typ161
Paper D
162
Simulation based Evaluation of Fault Detection Algorithms
Knowledge
Input
Factors
z = [f , dT ]T
Monitored
System
Observations
Feature
Extraction
Features
Behavior
Comparison
Test
Quantity
q
Decision
Rule
Symptom
Fault Detection Algorithm
Figure 1: Overview of a fault detection scheme. The monitored system is
affected by input factors and generates observations. Features are extracted
from the observations which are compared against reference (known) behaviors of the features to generate test quantities. A decision rule determines
which behaviors better explain the observations, i.e., it generates a symptom.
ically makes use of several fault detection methods, each containing partial information of the system. In fault diagnosis, the different symptoms are processed
to generate a statement of the state (condition) consistent to all observations and
knowledge embedded in the diagnosis solution.
While increasing the amount of symptoms used for fault diagnosis may increase
the quality of the diagnosis process, it is clear that the accuracy of the symptoms
is crucial. Design and evaluation of fault detection methods are therefore important. Figure 1 shows an overall scheme of a fault detection scheme. The monitored system is affected by input factors which are relevant for the diagnosis, e.g.,
faults and disturbances, and generates observations. The observations are processed to extract relevant features that can describe the status of the system (e.g.,
parameters, residuals, signal spectra). The behavior of the features are then compared to (known) reference behaviors (e.g., based on distances) to generate a test
quantity. The combined tasks of feature extraction and behavior comparison is
denoted fault detection algorithm (fda). Finally, a decision rule (e.g., a threshold
check or a statistical test) is used to accept or reject the reference behaviors that
the test quantity can explain, i.e., it generates a symptom.
1.1 Problem description and motivation
The accuracy of the symptoms generated by the fault detection is determined
by the ability of the test quantity generated by the fda to relate to changes in
the system behavior. It is thus natural to evaluate fault detection methods based
on the test quantities alone, independent of the decision rule used. This is for
instance in line with the theory of statistical hypothesis testing, when an optimal
test is given by the likelihood ratio test and is determined only by the statistical
behavior of the test quantity, see, e.g., Basseville and Nikiforov (1993).
A test quantity, denoted q, measures deviations from one or more reference behaviors. The reference behaviors are associated to states of the system, e.g., healthy or
faulty. In this work, the focus is on the analysis of a single fault, denoted f . Rather
than considering test quantities which are time sequences, e.g., model residuals,
the focus is restricted to batch fault detection algorithms, which produce a scalar
1 Introduction
163
q for an entire data batch. Batch methods are common for signal/data-driven approaches and parameter estimation, but similar ideas could be used also for time
sequences by summarizing the sequence to a scalar, e.g., by considering steadystate values or some norm.
In practice, the data input to fault detection (and thus q) are not only affected
by the fault f but by a collection of n factors z. In this paper, the input factors
considered in the examples are a composition of the fault f and sources of disturbances d, i.e., z = [f , d T ]T , where d = [d1 , · · · , di , · · · , dn−1 ]T . The disturbances,
d, may cause undesired variations to q, deteriorating its capacity to distinguish
changes in f and thus complicating a decision. Under specified conditions and assumptions, optimality of fdas might be possible, see, e.g., Liu and Zhou (2008);
Li and Zhou (2009); Frank and Ding (1997); Wei and Verhaegen (2011), and it
may be possible to compare different schemes (Isermann, 1994, 2006). However,
since only partial knowledge of the system can be embedded in any fda, it is important to evaluate it in scenarios which are relevant for its practical use. From a
practical perspective, given a complex system and candidate fdas, the following
questions are of relevance:
Q-1 Which factors in d affect q the most? And should therefore be given more
relevance for further development of the fda.
Q-2 How can test quantities generated from different fdas be compared and
evaluated against each other to enable selection of the “best” fdas?
Q-3 What is the effective scope of an fda? That is, for what region in the z space
is the ability of q to relate to f satisfactory?
Notice that the focus is not on properties of a particular fda but to define approaches to evaluate and compare any fda.
These questions can be addressed at different levels of closeness to the real application. Level 0 corresponds to the ideal case where the fdas are evaluated
with operational data. This is particularly difficult since it may take extremely
long times for faults to appear. To overcome this, data can be collected from
experiments performed in a lab, where faults and disturbances are induced, corresponding to Level 1 studies. Even at Level 1, an extensive evaluation is often
inviable due to the extreme costs and time required. Furthermore, it is often the
case that all (or parts of) the factors z are unmeasurable and therefore a complete
analysis based on real data is difficult. At Level 2, data are generated based on
simulations of the monitored system, which is a more viable alternative. The
simulation study must, on the other hand, be designed carefully so that it is representative of practical scenarios.
1.2 Main contributions and outline
In this paper, ideas inspired by the literature of design of experiments, surrogate models, sensitivity analysis and change detection are presented to address
these questions based on simulation studies. Even in simulation studies, an extensive analysis of the effects of z to q may exhaust the computational resources
and time available. An important idea considered here is to bypass the need for
164
Paper D
Simulation based Evaluation of Fault Detection Algorithms
simulation/experimental data using a surrogate (or meta) model. Different types
of surrogate models are possible, e.g., based on neural networks and Gaussian
processes. For its simplicity and tractability, the surrogate models considered
here will take the form of a linear regression,
q = φ(z)T θ + ǫ,
φ( · ) : Rn 7→ Rnθ ,
q, ǫ ∈ R
(1)
where the regressors function φ( · ) makes a direct map from z to q through the
regression coefficients θ and ǫ is an additive uncertainty term. The surrogate
model incorporates both the monitored system and fda. Studies based on surrogate models are denoted as Level 3. In such approach, the choice of factors z
and regressors, the identification of θ and model validation are important and
are subject of study in the field known as design of experiments (doe) which is
briefly described in Section 2.
An answer to Q-1 is presented in Section 3, where the coefficients θ of the regression models are studied using sensitivity analysis to determine which factors
in d affect q the most. A main advantage with the use of surrogate models is that
Monte Carlo (mc) simulations can be performed efficiently. mc runs are used in
Section 4 to evaluate a measure of average effects of changes in q by f which is
used to address Q-2. In Section 5, a measure of satisfactory performance is suggested which is evaluated with mc runs under various combinations of z in an
attempt to answer Q-3. In Section 6, the ideas are illustrated for the evaluation of
methods used for wear diagnosis in industrial robots. Relevant characteristics of
the problem and methods are revealed from the study. Concluding remarks are
given in Section 7.
2 Design of Experiments
Design of experiments (doe) can be applied to any system where the experimenter has control over the input variables, or input factors, and that the output
can be measured (Kleijnen et al., 2005). Here, the object of study is an fda applied in combination to a monitored system. The input factors are, e.g., faults
and disturbances that may affect the behavior of the monitored system, and the
output is the test quantity q. The next sections are organized to give an introduction to the field, for more details see, e.g., Box et al. (1978); Kleijnen et al. (2005);
Sanchez (2006).
2.1 Choice of input factors
The first task in designing an experiment is the selection of the input factors
z and their possible range of values. Factors can be included according to the
objectives of the study, to verify or falsify assumptions about the behavior of the
test quantity and to study their relations in detail. The choice of factors should
be performed carefully, with the help of experts in the application, since a poor
specification may generate misleading results. For the examples presented here,
input factors include the fault, f , and disturbance factors, d, but other types of
2 Design of Experiments
165
factors could be included such as initial conditions, tuning variables, etc.
Once the factors are chosen, the experimenter must decide their range of values
and a discrete set of factor levels that shall be considered in the study. A more
detailed study is possible by increasing the number of levels, m, in a compromise
with the number of experiments required. The factor levels chosen will have
an impact on the study and it is therefore important to choose levels which are
extreme but not impossible for realistic situations. Two representations of factor
levels are typically used:
• natural levels are the values for the factors that are used in the experiment
or simulation;
• coded levels, all factors are normalized to the same scale. Used when identifying the surrogate models.
The normalization of the coded levels is important because otherwise the parameters of the surrogate model would be affected by the scaling of the factors.
2.2 Surrogate models as linear regressions
Linear regression models as in (1) are simple, tractable and easy to interpret. For
these reasons, they are a popular choice in the doe literature. A limitation is
that they may misrepresent the relations between z and q. To circumvent this,
more complex model structures, such as neural networks and Gaussian processes
could be considered, see, e.g., Oakley and O’Hagan (2004). Compared to linear
regressions, more complex models may be less interpretable and tractable which
are important characteristics for surrogate models. Easy to interpret models are
particularly important for sensitivity analysis, studied in Section 3.
Many different model structures of linear regressions can be considered. From
the doe literature, two structures are commonly used. A main effects model has
regressors that are directly dependent on the inputs factors, i.e.,
φ(z)T = [ 1,
T
θ = [ b,
z T ] = [ 1,
{ηi } ]
f,
d1 , · · · ,
dn−1 ]
(2a)
(2b)
where b is a bias term and ηi has indices i ∈ {0, · · · , n−1}. Since this is a simple
model, it may not be a realistic representation of the system. A second-order
model extends the main-effects model with interaction (cross) and quadratic
terms as
φ(z)T = [ 1,
T
θ = [ b,
z T , svec(zz T )T ]
{ηi },
{ηij },
{ηii } ]
(3a)
(3b)
where svec( · ) maps a symmetric matrix of size N to a vector of length N (N + 1)/2
and i, j ∈ {0, 1, · · · n−1} with i > j. A second order model can capture more complex
relations between the factors than a main effects model. However, since each
factor is included in several terms, it is more difficult to analyze the effects of
different factors to q. Notice that the models can be extended further with any
type of relation between the factors.
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2.3 Identification
Consider that N experiments are performed with inputs
ZN = [z 1 , · · · , z N ]T ∈ RN ×n
(4)
and outputs q ∈ RN . Given that the test quantities can be described by (1), the
resulting model is q = Φ(ZN )θ + ǫ with Φ(ZN ) , [φ(z 1 ), · · · , φ(z N )]T . To find the
coefficients θ, a least-squares error criterion gives
b
θ = arg min kq − Φ (ZN ) θk22
θ
= RN (ZN )−1 Φ(ZN )T q,
T
RN (ZN ) , Φ (ZN ) Φ (ZN ) .
(5)
(6)
The estimate is unbiased and consistent in case the errors are uncorrelated with
the regressors and has finite variance (see Ljung (1999)). For N observations, an
estimate of the covariance of b
θ is found through the expression (see Ljung (1999))
bb = γ
b RN (ZN )−1
P
(7)
θ
2
1
q − Φ (ZN ) b
b,
θ2 ,
(8)
γ
N −n−1
b is an estimate of the error variance. Notice again that the coded levwhere γ
els should be used when identifying the regression coefficients. Otherwise, the
scaling of the variables will hinder some of the analysis presented further.
2.4 Design matrix
A design matrix represents the user choice of simulation experiments to be performed. Typically, the columns correspond to the factor levels and rows are design points, i.e., a specific choice of the coded levels z. Using the previously
introduced notation, a design matrix corresponds to a specific choice of ZN in (4).
Some designs are briefly described below.
Full Factorial Design, N = mn . In this design, the m possible levels for the n
factors are combined exhaustively, so the design matrix has N = mn design points.
The number of experiments increases rapidly with n, e.g., for m = 5, n = 6, a total
of N = 15625 experiments are needed. This design is rather conservative and
more efficient designs are available.
Fractional Factorial Design, N = mn−f . To reduce the complexity of a full factorial design, it is possible to carefully select subsets of a full design, where mf is
the fraction removed.
Central Composite Design. These designs are useful to efficiently identify higher
order models (Box and Wilson, 1951). The design is usually based on a full or
fractional factorial. The base factorial design is then extended by adding a center point and two “star points” for each factor. This design is illustrated by the
following example.
2 Design of Experiments
167
Example. A central composite design for n = 3 factors based
on 23 full factorial design (black), a center point and star
points at the faces (gray).
z3
z2
z1
Optimal Designs. By noting that the covariance of b
θ given in (7) depends on
RN (ZN ), it is natural to define minimum-variance designs, that minimize some
measure of RN (ZN ). Different types of optimal designs are possible depending on
the measure used, a D-optimal design will minimize the determinant of RN (ZN )
while an A-optimal design will minimize its trace. For more on optimal designs,
see, e.g., Fedorov (1972); Atkinson et al. (2007).
2.5 Design parameters
The validity of surrogate models is limited since the behavior of the test quantities can differ considerably depending on different variables. For example, it
should not be expected that the same surrogate model can be used to describe
test quantities from different fdas or when applied to different monitored systems. The settings that determine the validity of the surrogate models are called
design parameters and one surrogate model should be identified for each different combination of design parameters considered in the study.
2.6 Model validation
The analysis performed in this paper are based on surrogate models and it is
therefore important to validate them. Model validation is used to assess whether
the model will generalize to input values independent of those used during the
model identification. Model validation can be done by cross-validation, where a
v
fresh dataset, denoted ZN
, is used with the sole purpose of validation.
v
The discrepancies between the model and the system behaviors can be studied
through the residuals, ε, which are defined as the differences between the system
and model outputs,
ε = q −b
q,
v b
b
q , Φ(ZN
)θ,
v
(9)
v
where ZN
represents a validation data set. If the residuals are small, the model
v
is considered valid, otherwise it is invalidated and perhaps a different model
structure should be considered.
The model fit (Ljung, 1999) can be used as a criterion to assess the validity of a
model. It is defined as
!
Nv
kεk22
1 X
fit = 100 1 −
,
s
,
si .
(10)
Nv
kq − sk22
i
The model fit relates to how well the model predicts the output in average.
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3 Determining Relevant Factors
An approach to address Q-1 is to study how changes in a factor affect the test
quantity q. The partial derivatives of the surrogate model with respect to the
factors z reveal how the first order properties of q are affected by z. This type
of study is part of sensitivity analysis (Saltelli et al., 2008). For the main-effects
model (2), the partial derivatives are given by
iT h
i
∂
∂
∂ h
1, z
b, η = η T ,
q(z) =
φ(z)T θ =
∂z
∂z
∂z
and the first order effects of factor zi to q are given directly by the associated ηi .
Since coded (normalized) levels of z are used, an inspection of the magnitude of
the elements of η can be used to find which factors affect q the most.
For more complex models, such as the second-order model (3), the partial derivatives depend not only on η but also on the values of z where they are evaluated.
Therefore, a direct comparison of the coefficients η does not have the same character as for a main effects model. However, for simple regression models, an
inspection of η can still provide useful insights about the effects of z to q.
3.1 Normalization of coefficients
For regression models where the fault has a direct term as
φ(z)T = [ 1,
T
θ = [ b,
f , ··· ]
η0 , {η}i>0 ]
(11a)
(11b)
the coefficient η0 relates to the direct effect of the fault f to q. To facilitate the
study and comparison of coefficients, the identified coefficient vector b
η for models of the form (11) can be normalized as
η=b
η /b
η0 .
(12)
In this manner, the normalized coefficients have
values relative to the direct effect
of the fault f . A normalized coefficient with ηi < 1 would thus mean that f has
a direct effect to q which
is larger than that caused by the regressor associated
with ηi . The case ηi > 1 is possible but undesirable (unless ηi also relates to f ).
Notice that η0 = 1.
3.2 Group analysis
The normalized coefficients in (12) can be grouped together over a subset of the
design parameters to investigate different aspects of the problem. For example,
consider a problem with two design parameters corresponding to the fda used
and the monitored system. Groups formed for each fda over all monitored systems would allow for an overall comparison of the fdas sensitivity. On the other
hand, groups formed for each monitored system could be used to reveal which
systems are more difficult to perform fault detection, independent of the fda
chosen.
4 Comparing Fault Detection Algorithms
169
Suppose there are K groups, where each kth group has Nk regression models. The
following matrix can be formed for the kth group
h
iT
Hk = η1 , · · · , ηNk
∈ RNk ×Nη .
(13)
Each group can be analyzed using box plots for each column of Hk , i.e., each type
of coefficient. This type of analysis is illustrated further in Section 6.2.
4 Comparing Fault Detection Algorithms
A simple approach to address Q-2 is to analyze the average effects a change in f
gives to q when random changes of the disturbance factors d are present. To
proceed, a change is defined in terms of hypotheses in Section 4.1 and a measure
of average change to the test quantity is defined in Section 4.2.
4.1 Two hypotheses
The performance of a test quantity is associated to how well it can be used to
relate the presence of a change from nominal in f , irrespective of variations in
the disturbances d. Given a test quantity q, two hypotheses are considered. The
null hypothesis, H0 , represents the case where q is collected when f is nominal
and the alternative hypothesis, H1 , states that an abnormal change in f is present.
These hypotheses can be described by the particular choices of input factors
H0 :
H1 :
f = f 0,
0
f = f + ∆,
d ∼ p(d),
d ∼ p(d),
(14a)
(14b)
where f 0 is the nominal value of f , ∆ is the fault change size and p(d) is a distribution for the (considered random) disturbance factors d. Test quantities collected
under the different hypotheses are denoted as q|H0 and q|H1 .
4.2 A measure of average effects
Denoting µi , σ i the mean and standard deviation of q|Hi , for the hypotheses
given in (14) the signal to noise ratio (snr) is defined as
µ1 − µ0
,
(15)
σ1
and relates to the average effects a change of size ∆ in f causes to the test quantity
in relation to effects of random variations in d. The larger the snr value, the easier it will be to distinguish the change in f . In order to find the quantities used
in the computation of the snr, Monte Carlo runs can be performed for different
realizations of d until enough samples of q|H0 and q|H1 are collected for the estimation of µ0 , µ1 , σ 1 . Here, the use of surrogate models instead of experiments
allows for efficient mc runs, and the quantities can be found accurately and in
short time. Notice that, in some cases, the snr can be found analytically, e.g.,
when a main-effects model is used and p(d) is the Gaussian distribution.
snr ,
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4.3 Group analysis
In a similar manner as discussed in Section 3.2, the snrs can be grouped over
subsets of design parameters to asses different aspects of the problem. Notice
that the snrs are already normalized quantities. The use of snrs for comparison
of fdas is illustrated in Section 6.3.
5 Determining the Effective Scope
To address Q-3, a measure of satisfactory performance of a test quantity should
be defined. Once the performance criterion is defined, it is possible to investigate
what region in the z space is the criterion fulfilled. That is, the effective scope of
the test quantity can be found.
5.1 A measure of satisfactory performance
The behavior of a test quantity should be such that it allows for an accurate decision of whether a change in f is present or not. Presence or absence of a change
in f can be described by the two hypotheses in (14). The snr, introduced in Section 4, can be used as a performance measure since it relates to how accurate a
decision can be made. The performance of an fda can be considered satisfactory
in case the snr is large enough, e.g., written as

′


1, if snr ≥ snr ,
pass = 
(16)

0, otherwise
where snr′ is a lower limit for the snr. This criterion is simple to evaluate but
since no decision mechanism is defined, it is an indirect measure of performance
for the fault detection.
For a given decision rule, the accuracy of the fault detection can be defined in
terms of the probabilities of false detection Pf , i.e., accepting H1 when H0 is
true, and correct detection Pd , i.e., accepting H1 when H1 is true. A natural
performance criterion is thus defined according to acceptable levels of Pd and Pf .
This can be tested with the function

′
′


1, if Pf ≤ Pf and Pd ≥ Pd ,
pass = 
(17)

0, otherwise
where Pf′ and Pd′ are the chosen performance requirements. For a satisfactory
performance of the test quantity, low Pf and high Pd are typically desirable.
The probabilities Pf and Pd are however dependent on the decision rule used.
Different decision rules are possible, see, e.g., Gustafsson (2000). Here, a threshold check is considered since it is one of simplest and is also a common choice. It
is defined as
n
o
Choose H0 if: s ≤ ~. Otherwise, choose H1 ,
(18)
5 Determining the Effective Scope
171
where ~ is a threshold. For the threshold check (18) with threshold value ~, Pf
and Pd can be computed as
Pf =
Z∞
0
p(q|H ) dq,
Pd =
~
Z∞
p(q|H1 ) dq.
(19)
~
where p(q|H0 ) and p(q|H1 ) denote the probability densities of q under the different hypotheses. Notice that according to (19), for a fixed Pf there is an associated
~ and thus a Pd . The criterion (17) can therefore be verified by first finding ~
for the limiting value Pf′ , computing the associated Pd and checking whether it is
larger than Pd′ . The hypotheses densities can be estimated given a large number
of observations for q|H0 and q|H1 , which can be achieved efficiently with mc runs
using surrogate models.
5.2 Finding the effective scope
To find the scope of a test quantity, the chosen criterion for satisfactory performance, e.g., (16) or (17), can be verified for multiple setups of the hypotheses in
(14). In order to simplify the analysis, one disturbance factor is varied randomly
at a time while the others are kept constant. This setup can be described by the
hypotheses
H0 : f = f 0 ,
1
0
H : f = f + ∆,
dj,i = dj′ ,
dj,i =
dj′ ,
di ∼ p(d)
di ∼ p(d)
(20a)
(20b)
i.e., the ith disturbance factor is varied randomly while the remaining are kept
constant. By checking the criterion for different choices of ∆ and p(d) in (20), it
is possible to gather understanding of the effective scope of the test quantity.
With this purpose, it might be useful to restrict how the distribution p(d) can
be varied. Consider for instance that p(d) has zero mean and variance σ 2 . By
varying σ, it is then possible to study how much variability of di is allowed for
a satisfactory performance. Considering that ∆ and σ can be chosen from the
vectors
h
iT
h
iT
(21)
∆ = ∆1 , · · · , ∆N∆ , σ = σ1 , · · · , σNd ,
all possible combinations of ∆ and σ define a grid of size N∆×Nd . The satisfactory
performance criterion for each pair (∆, σ) in the grid can be stored in a binary
matrix of the same size, denoted scope matrix. Because each entry in a scope
matrix relates to whether the performance criterion is achieved, its inspection
allows for a straightforward analysis of the scope of a test quantity.
5.3 Group analysis
Scope matrices can be found for each regression model. In a similar manner as
discussed in Sections 3.2 and 4.3, scope matrices can be grouped over subsets of
design parameters. Because each entry in the matrices is either zero or one, the
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information in the group can be summarized by summing over its scope matrices.
In this case, the entry values of the resulting group scope matrix will correspond
to how many times has successful performance been achieved for the corresponding combination of ∆ and σ over the design parameters in the group. This type
of analysis is illustrated in Section 6.4 for the robotics application.
6 Evaluation of Fault Detection Algorithms for Wear
Monitoring in Robots
The framework is illustrated for the problem of wear diagnosis in an industrial
robot joint. As empirically shown in Bittencourt and Axelsson (2014) from accelerated wear tests (Level 1 studies), wear in a robot joint can lead to variations of
friction. Since the friction torques must be overcome by the motor torques during operation, it is possible to extract information about friction (and wear) from
available signals. Friction is however dependent on other factors than wear, such
as temperature and load. The effects of temperature are specially difficult since
temperature is not measured in typical robot applications. These effects should
nevertheless be considered when evaluating different fault detection algorithms.
To simplify the presentation and due to confidentiality issues, the fdas considered in the study are treated as black-boxes, processing data to generate a test
quantity q, recall Figure 1. The focus is placed on the evaluation and comparison
of the fdas. The fdas considered in this study share the following characteristics,
which are relevant for the presentation of the paper.
C-1
C-2
C-3
C-4
Process data batches collected from a test-cycle.
Output a scalar quantity for each data batch.
Require nominal (wear-free) data.
Process data for a single axis and should indicate wear changes only for that
axis.
C-5 The behavior of a test quantity depends on a combination of fda, robot,
axis and test-cycle.
Data for the study are collected at Level 2, i.e., based on simulation experiments,
using an abb internal simulation tool and the analysis results are achieved at
Level 3, with the use of surrogate models. A simplified version of the friction
model presented by Bittencourt and Axelsson (2014) is included in the simulation model. The model used to describe friction in the robot joints is given by the
static nonlinear function
!
ϕ̇
ϕ̇ − θ ̟ − θ +θ ξ 3
8
4
τf (ϕ̇, ξ, ̟) = θ0 + (θ1 + θ2 ξ)e
+ +θ7 e
sign(ϕ̇)
(22)
+(θ5 + θ6 ξ + θ9 ̟)ϕ̇,
and relates to the effects of angular speed, ϕ̇, temperature (as measured in the
joint lubricant), ξ, and wear fault, ̟, to friction, τf . The remaining variables θ
are model parameters, see Bittencourt and Axelsson (2014).
6 Evaluation of fdas for Wear Monitoring in Robots
173
The complete setup for the study includes.
S-1 Three fdas for wear monitoring, denoted A, B and C.
S-2 Two robot simulation models, corresponding to a medium sized robot with
max. payload of 10-25kg and of a large robot with max. payload of 100250kg.
S-3 Wear is studied in the first three axes of these robots.
S-4 A total of six different test-cycles.
The study is aimed at answering questions Q-1 to Q-3 for the robotics application.
The next sections define the experiments performed and present the results.
6.1 Design of experiments
Input factors
The following factors are considered relevant and are included in the study.
Wear. According to S-3, wear is introduced in three of the axes. Recalling that
the fdas process data for a single axis (C-4), the wear introduced in that axis will
correspond to the fault factor f . When wear is present in the other two axes, they
may cause variations in q due to coupling effects. Since these variations may complicate fault isolation, they are considered as disturbances, d1 and d2 . The wear
̟ in (22) is a dimensionless quantity with values between 0 (no wear) and 100
(a total failure due to wear), see Bittencourt and Axelsson (2014) for details. In
this study, it is considered that values in the range [0, 50] are of interest. This is
because the detection of a partial failure is more interesting for condition-based
maintenance since it gives enough time to perform maintenance before a failure.
Temperature. The friction model used given in (22) includes temperature dependencies which will affect the data used for the fdas. The temperature factor is
assigned as d3 . The temperature range considered is [30, 70] ◦ C and is based on
a typical temperature behavior for a robot operating in a room with controlled
environment temperature. The range copes with variations due to self-heating
caused by losses in the joint and changes in the environment temperature.
Point-to-point delay. In point-to-point movements, the robot is required to fulfill
a set of criteria in order to guarantee that a certain position was reached before
issuing a command to move to the next position. During real-time path execution, the time required for the verification of these criteria may differ, causing
variations to the trajectory. This varying “delay” is expected to have an effect on
the test quantities and is thus included as a factor, d4 . The range of values for d4
is [25, 75] ms and is based on values found for the robots studied.
Payload mass error. The control system used in the robot relies on the defined
payload mass. The closed-loop system (and data) will thus be affected in case
there is an error in the defined mass. The payload mass error is assigned as d5
and has values in the interval [−10, 10] % relative to the correct mass.
For the study, five levels are considered for each factor, i.e., m = 5. The levels
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Table 1: Definition of the factor levels used.
Coded Levels
-2
-1
0
1
2
Factor
Natural Levels
f , d1 , d2 - wear
0
12.5 25 37.5 50
d3 - temperature
30
40
50
60
70
d4 - point-to-point delay 25 37.5 50 62.5 75
d5 - payload mass error
-10
-5
0
5
10
Table 2: Some entries of the design matrix.
Row f d1 d2 d3 d4 d5
...
1
-1 -1 -1 -1 -1 -1 41 0 0 0 0 -2
2
-1 -1 -1 -1 1 1 42 0 0 0 0 2
3
-1 -1 -1 1 -1 1 43 0 0 0 0 0
4
-1 -1 -1 1 1 -1 44 0 0 0 0 0
5
-1 -1 1 -1 -1 1 45 0 0 0 0 0
Unit
◦C
ms
%
0
0
-2
2
0
are distributed linearly within the suitable range for the factors. The factor levels
used can be seen in Table 1. According to C-3, the test quantities require nominal
(wear-free) data which are generated according to the following coded levels,
h
iT h
iT
z 0 = f , d1 , d2 , d3 , d4 , d5
= −2, −2, −2, 0, 0, 0
, (23)
i.e., no wear is present in any of the axes, with temperature at 50◦ C, 50 ms of
point-to-point delay and no error in payload mass.
Regression models
Two model structures are considered, a full second-order model as in (3) and a
simplified second order model of the form
ϕ(z)T = [ 1,
T
θ = [ b,
f,
d T , svec(dd T )T ]
η0 , {ηi },
{ηij },
{ηii } ]
(24a)
(24b)
where i, j ∈ {1, · · · , n} with i > j. Notice that there are no cross-terms for the
fault f in model structure (24), only for the disturbances d. An interpretation of
the coefficients for this model is thus simpler compared to the full second-order
model.
Design matrix
A central composite design based on a 2n−1 fractional factorial design with one
center point and star points at [2,-2] is considered, requiring a total of N = 45
experiments. Parts of the values for the design matrix are seen in Table 2.
6 Evaluation of fdas for Wear Monitoring in Robots
Table 3: Factor levels used for validation.
Factor
Natural Levels
f , d1 , d2 - wear
5 15 25 35 45
d3 - temperature
35 42 49 56 63
d4 - point-to-point delay 31 41 51 61 71
d5 - payload mass error
-9 -6 -3
0
3
175
Unit
◦C
ms
%
Table 4: Model fits for a robot and test-cycle.
Model Fit [%]
fda Model Eq. Axis 1 Axis 2 Axis 3
A
(24)
83.2
72.5
82.1
A
(3)
87.9
83.7
88.1
B
(24)
64.6
65.5
65.7
B
(3)
87.8
91.0
91.9
C
(24)
89.8
84.2
85.6
C
(3)
95.0
85.3
89.9
Design parameters
According to C-5, the test quantities produce comparable results only when the
same fda, robot, axis and test-cycle are used. Therefore, for different combinations of these design parameters, a different regression model should be used.
This gives a total of 3×2×3×6 = 108 regression models corresponding to the respective number of fdas, robots, axes and test-cycles considered. Notice though that
the same design matrices can be used to identify all regression models. And further that the same simulated data for a robot can be used to identify the models
for all fdas and for all axes. Each regression model requires N = 45 experiments,
a total of 108/(3 × 3) × 45 = 540 simulations are therefore needed to identify all
regression models in the study. Each simulation experiment takes around ten
seconds to be performed, requiring 1h30min for all 540 simulations.
Identification and validation
The simulation experiments are performed and the regression models are identified using (5). The design matrix used for identification, given in Table 2, is also
used for validation of the regression models but with different factor levels, given
in Table 3. The model fits, computed as in (10), are shown in Table 4 for a certain
robot and test-cycle. The fits are generally high for all fdas, with higher values
for the full second-order model, specially for fda B.
6.2 Determining relevant factors
Sensitivity analysis is used to address Q-1, i.e., to determine which input factors
cause more variations to the output of an fda. Because model (24) is simpler
to analyze than model (3), only the coefficients for this model are shown here.
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The regression coefficients are normalized as in (12) and are grouped for each
fda according to (13). Each of the group matrices have dimensions (36 × 21),
corresponding to the combination of the design parameters left and number of
coefficients in the regression model respectively.
In Figure 2, the 21 normalized coefficients are displayed in box plots for each test
quantity. The statistics for the box plots are computed over each column of the
group matrices. Recall that, because of the normalization used, coefficients with
magnitudes larger than 1 indicate that the corresponding regression term has a
larger effect to the output compared to the direct effect of f , i.e., wear. From the
figures, it is possible to note that the coefficient η3 considerably affects all fdas.
This coefficient relates to d3 , i.e., the direct effect of temperature. fda C presents
the lowest value for the median of η3 . Coefficient η5 , associated to d5 , i.e., payload
mass error, also gives significant responses for fda A but are generally small for
fdas B and C. The coefficients associated with the effects of wear in other joints,
d1 and d2 , and point-to-point delay d4 , show less significant responses for all
fdas.
Possible extensions
By including tuning variables as input factors to the surrogate models, the sensitivity of an fda to tuning can be investigated in the same manner.
The outliers present in the box plots for the coefficient groups should be investigated in detail as they may reveal important properties of the problem and fda.
There might be combinations of the design parameters that increases or reduces
the sensitivity to certain factors. This type of information is useful for further
development and verification of fdas.
6.3 Comparing fault detection algorithms
As discussed in Section 4, the snrs can be seen as an average performance measure for a test quantity. For the computation of the snrs, the parameters defining
the hypotheses in (14) are set as
f 0 = −0.5,
∆ = 1,
p(d) = N (0, I σ 2 ),
σ = 0.25,
(25)
i.e., the wear fault, f , is changed by a fourth of its allowed range and the disturbance factors d are considered as Gaussian random variables independently distributed with a common standard deviation which is 1/16 of their range. Model
structure (3) is considered in the study since it presented larger fits in general
(recall Table 4). For each regression model, the snrs are computed based on 1 105
mc runs. Using the surrogate models, the total 1.08 107 mc runs needed for all regression models took approximately 12 seconds in a standard desktop computer.
To perform the same analysis using Level 2 studies, i.e., with simulations, would
have taken nearly three and a half years.
The snrs grouped according to fda are displayed as box plots in Figure 3. The
snrs can be used to rank the different fdas. If the median over each group is
used as a criterion, this example reveals that fda C gives the best performance.
6 Evaluation of fdas for Wear Monitoring in Robots
177
3
ηij
2
1
0
-1
5,5
4,4
3,3
2,2
1,1
4,5
3,5
3,4
2,5
2,4
2,3
1,5
1,4
1,3
1,2
0 1 2 3 4 5
index i, j
(a) fda A.
2
1.5
ηij
1
0.5
0
-0.5
-1
5,5
4,4
3,3
2,2
1,1
4,5
3,5
3,4
2,5
2,4
2,3
1,5
1,4
1,3
1,2
0 1 2 3 4 5
index i, j
(b) fda B.
2
1.5
ηij
1
0.5
0
-0.5
5,5
4,4
3,3
2,2
1,1
4,5
3,5
3,4
2,5
2,4
2,3
1,5
1,4
1,3
1,2
0 1 2 3 4 5
index i, j
(c) fda C .
Figure 2: Normalized regression coefficients for model structure (24)
grouped according to fda. In the box plots, the dotted circle indicates the
median, the extremities of the bar relate to the 25th and 75th percentiles and
the isolated circles are outliers. Notice the different scales.
Paper D
178
Simulation based Evaluation of Fault Detection Algorithms
A
B
C
-2
0
2
4
6
8
10
12
14
16
18
Figure 3: snrs grouped for the different fdas. The box plots are for groups
over all design parameters where the dotted circle indicates the group median, the extremities of the bar relate to the 25th and 75th percentiles and
circles are outliers.
Possible extensions
For some design parameters, fda A gives very large snr values. These cases
correspond to outliers in the box plots and could be investigated further as they
may reveal useful information about the fda.
This type of analysis can also provide criteria for the choice of design parameters.
For instance, the snrs can be grouped according to test-cycles to reveal whether
there are test-cycles that facilitate the distinction of faults in general or for a specific fda. Tuning variables can also be seem as a design parameter, in which
case different regression models are found for different values of the tuning parameters. In this setting, the snrs for a certain fda can be grouped according
to the tuning parameters, providing a criterion for the selection of the tuning
parameters.
6.4 Determining the effective scope
The use of scope matrices is illustrated here to determine how the factors d3 and
d5 , i.e., temperature and payload mass error, delimit the scope of the test quantities. Due to its natural interpretation, criterion (17) is considered with Pf′ = 0.01
and Pd′ = 0.99 for a decision rule given by the threshold check (18). The hypothe′
ses in (20) are defined with f 0 = −2, dj,i
are set to the nominal values given in (23)
and di ∼ p(d) = N (0, σ 2 ) is a Gaussian distribution with zero mean and standard
deviation σ. The criterion is evaluated for values of ∆ ∈ [0, 4] and σ ∈ [0.01, 1]
based on a linear grid of size 30×30. The hypotheses densities are estimated using a kernel density estimator based on 1 105 mc runs. The total mc runs needed
for the study is of 30×30×1.08 107 = 9.72 109 which took approximately 3h15min
using the surrogate models. To evaluate the analysis at Level 2, with simulations,
would have taken more than three millennia.
Group scope matrices are formed for each fda. An entry in the resulting matrix
6 Evaluation of fdas for Wear Monitoring in Robots
4
A
4
B
179
4
C
3
3
3
∆ 2
1
2
2
1
1
0
0.01 0.34 0.67 1
σ
0
0.01 0.34 0.67 1
0
0.01 0.34 0.67 1
(a) Random temperature disturbances d3 .
4
A
4
B
4
C
3
3
3
∆ 2
1
2
2
1
1
0
0.01 0.34 0.67 1
0
0.01 0.34 0.67 1
0
0.01 0.34 0.67 1
σ
(b) Random payload mass error disturbances d5 .
Figure 4: Visualization of the scope matrices grouped according to fda. The
colormap relates to how often the performance test was successful, varying
from 0 (black) to 36 (white). The clearer the plot, the more often an fda
performed satisfactorily.
180
Paper D
Simulation based Evaluation of Fault Detection Algorithms
can take values between zero and 36. The resulting matrices for disturbances of
temperature, d3 , and payload mass error, d5 , are shown in Figure 4 with a colormap associated to the entry value in the scope matrix. The clearer the graph,
the more often the fda performs satisfactorily for the related combination of
change size ∆ and standard deviation of disturbance σ. From an inspection of
the figures, it is possible to determine the minimal size of ∆ for which an fda performs satisfactorily given a fixed disturbance variation σ, and vice-versa. From
Figure 4b, it is possible to note that fda C is the least affected by payload disturbances. As seen in Figure 4a, all test quantities are considerably affected by
temperature, but fdas A and C allow for more variations of temperature compared to fda B.
Possible extensions
The hypotheses defined in (20) allow for variation of only two factors at a time,
the fault f and a disturbance factor of choice di . In principle, any number of
the n factors can be changed at the same time. The same type of analysis can be
considered, although alternative visualization techniques may be needed.
7 Conclusions
This paper proposed a framework for evaluation and comparison of fault detection algorithms (fdas) based on simulations. An extensive investigation of the
different fdas is made possible with the use of surrogate models which considerably reduces the time needed for the evaluation study. As illustrated in the
application example, this was in fact the only viable alternative. The approaches
suggested may be used to reveal which inputs affect an fda the most, which fda
performs best in average and the effective scope of an fda. The framework is
rather general and can be extended to study various aspects of fault detection
algorithms.
It should be stressed that conclusions drawn based on simulations or surrogate
models should always be carried out carefully since they are a limited representation of reality. Results achieved in this manner give good insights about the problem and support decisions but, ultimately, the fault detection algorithms should
be evaluated based on real experiments. In the robotics application, accelerated
wear tests can be used with this purpose, but with much higher costs and time
required for a statistically significant study.
Bibliography
181
Bibliography
A. Atkinson, A. Donev, and R. Tobias. Optimum Experimental Designs, with SAS.
Oxford University Press, Cary, USA, 2007.
M. Basseville and I. V. Nikiforov. Detection of abrupt changes: theory and application. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1993.
A. C. Bittencourt and P. Axelsson. Modeling and experiment design for identification of wear in a robot joint under load and temperature uncertainties based
on friction data. IEEE/ASME Transactions on Mechatronics, 19(5):1694–1706,
October 2014.
G. E. P. Box and K. B. Wilson. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B (Methodological), 13
(1):1–45, 1951.
G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters. John
Wiley and Sons, 1978.
V. V. Fedorov. Theory of optimal experiments. Academic Press, 1972.
P. Frank and X. Ding. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. Journal of Process Control, 7(6):
403–424, 1997.
F. Gustafsson. Adaptive Filtering and Change Detection. Wiley, October 2000.
R. Isermann. On the applicability of model-based fault detection for technical
processes. Control Engineering Practice, 2(3):439–450, 1994.
R. Isermann. Fault-Diagnosis Systems - An Introduction from Fault Detection to
Fault Tolerance. Springer, 1st edition, 2006.
J. P. C. Kleijnen, S. M. Sanchez, T. W. Lucas, and T. M. Cioppa. A User’s Guide to
the Brave New World of Designing Simulation Experiments. INFORMS Journal
on Computing, 17, 2005.
X. Li and K. Zhou. A time domain approach to robust fault detection of linear
time-varying systems. Automatica, 45(1):94–102, 2009.
N. Liu and K. Zhou. Optimal robust fault detection for linear discrete time systems. Journal of Control Science and Engineering, 7:1–16, January 2008.
L. Ljung. System Identification: Theory for the User. Prentice Hall PTR, 2nd
edition, December 1999.
J. E. Oakley and A. O’Hagan. Probabilistic sensitivity analysis of complex models:
a Bayesian approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(3):751–769, 2004.
A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Carboni, M. S. D. Gatelli, and
182
Paper D
Simulation based Evaluation of Fault Detection Algorithms
S. Tarantola. Global Sensitivity Analysis. The Primer. John Wiley and Sons,
2008.
S. Sanchez. Work smarter, not harder: guidelines for designing simulation experiments. In Proceedings of the 2006 Winter Simulation Conference, Monterey,
USA, December 2006.
X. Wei and M. Verhaegen. Robust fault detection observer design for linear uncertain systems. International Journal of Control, 84(1):197–215, 2011.
Paper E
Data-Driven Anomaly Detection
based on a Bias Change
Authors:
André Carvalho Bittencourt and Thomas Bo Schön.
Edited version of the paper:
A. C. Bittencourt and T. Schön. Data-driven anomaly detection based
on a bias change. In Proceedings of the 19th IFAC World Congress,
Cape Town, South Africa, 2014.
Data-Driven Anomaly Detection based on a
Bias Change
André Carvalho Bittencourt∗ and Thomas Bo Schön∗∗
∗ Dept.
∗∗ Dept.
of Electrical Engineering,
Linköping University,
SE–581 83 Linköping, Sweden
of Information Technology
Uppsala University
SE-751 05 Uppsala, Sweden
Abstract
This paper proposes batch and sequential data-driven approaches to
anomaly detection based on generalized likelihood ratio tests for a
bias change. The procedure is divided into two steps. Assuming availability of a nominal dataset, a nonparametric density estimate is obtained in the first step, prior to the test. Second, the unknown bias
change is estimated from test data. Based on the expectation maximization (em) algorithm, batch and sequential maximum likelihood
estimators of the bias change are derived for the case where the density estimate is given by a Gaussian mixture. Approximate asymptotic
expressions for the probabilities of error are suggested based on available results. Simulations and real world experiments illustrate the
approaches.
1 Introduction
In anomaly detection, the main objective is to determine whether observations
collected from a system conform to expected (normal) behavior or not (i.e., an
anomaly). Anomaly detection appears in a variety of applications, such as condition monitoring of machines, fraud detection, intrusion detection, etc. A survey
in the topic is provided by Chandola et al. (2009). A factor that distinguishes
anomaly detection to related detection problems is the lack of knowledge of the
anomaly. This is a rather common situation, e.g., in condition monitoring and
fault detection. In many cases, anomaly detection is done with the use of a statistical model to describe the nominal behavior of the observations. An anomaly
can be inferred in case the observed data do not conform to the model. In many
practical situations, however, it can be difficult to determine the statistical model
for the observations. A common situation is that it is possible to collect measurements (data) under normal conditions. This nominal dataset contains relevant
information about the conforming behavior of the system and it is possible to
infer the presence of an anomaly based only on nominal data.
185
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Data-Driven Anomaly Detection based on a Bias Change
Examples of data-driven approaches to anomaly detection are one-class classification algorithms, e.g., Devroye and Wise (1980); Schölkopf et al. (2001), where
a boundary region in the observation space is determined from a nominal dataset.
Fresh observations falling outside this region are classified as anomalies. A shortcoming with such an approach is that all knowledge about the normal behavior
is summarized by a region in the observation space. For instance, this approach
would fail to recognize that if observations consistently fall in a low probability
region of the support, it is more likely that an anomaly is present. An alternative
is to estimate a model of the measurements density based on the nominal data. In
this case, anomalies can be detected based on the probability that test data have
under the estimated density model. Since it is often difficult to determine the
family of distributions, flexible density models are commonly used, such as mixture models (Agarwal, 2007), as well as nonparametric estimates (Desforges et al.,
1998; Yeung and Chow, 2002). A shortcoming with approaches based on a model
solely for the nominal behavior is that it is not possible to provide an estimate of
how certain the test is of the presence of an anomaly. This type of information is
however often important in practice to support decisions of recovery actions.
With the possibility to determine probabilistic models for both the normal and abnormal behaviors, it is possible to quantify the decision uncertainties since probabilistic models are defined for the entire problem. Anomaly detection can be
seen as a hypothesis testing problem (htp). In a binary htp, the null hypothesis
H0 describes the nominal behavior and the alternative hypothesis H1 describes
the abnormal behavior. The hypotheses are described by the statistical behavior
of the measurements y ∈ Rd under each hypothesis,
H0 : y ∼ p 0 (y),
H1 : y ∼ p 1 (y).
(1)
When the hypotheses densities, p 0 (y) and p 1 (y), are given or when their family
of parametric distributions are known, there are well-established statistical tests
based on likelihood ratios, i.e.,
Λ(y) , p 1 (y)/p 0 (y),
(2)
see, e.g., Neyman and Pearson (1933); Wald (1945).
An approach to overcome the lack of knowledge for the anomaly is to define it
as a change relative to nominal. In this manner, the available knowledge about
the nominal behavior can be used to test for an anomaly. Here, a bias (location)
change is considered, i.e., the density for the alternative hypothesis is written as
p 1 (y) = p 0 (y − ∆),
for an unknown bias change ∆. Using this model, this article aims at providing an
approach for anomaly detection that without requiring specification of a density
function and based only on availability of a nominal dataset,
• is flexible and can be used for different problems,
• can provide estimates of the decision uncertainties,
• requires only minimal and meaningful specification parameters.
2 The Bias Change Model and the glr test
187
This is achieved via a two step approach. First, the nominal dataset is used to find
a nonparametric estimate of the density function for H0 , denoted b
p 0 (y). In the
second step, incoming test measurements are used to find a maximum likelihood
estimate b
∆ of the unknown bias change. These estimates are used to define the
approximate models
H0 : y ∼ b
p 0 (y),
1
1
(3a)
0
H :y∼b
p (y | b
∆) = b
p (y − b
∆),
(3b)
which are tested based on a generalized likelihood ratio (glr) test assuming this
model to be true. Both batch and sequential tests are devised.
The presentation is organized as follows, Section 2 presents the bias change model
and reviews the glr test. Section 3 presents the approaches used to find the estimate b
p 0 (y) based on a nominal dataset. The resulting density model will be a
finite mixture distribution. Section 4 defines maximum likelihood estimators for
∆ based on the Expectation Maximization algorithm. Algorithms are derived for
mixtures of the multivariate Gaussian distribution. The use of glr tests based on
the approximate models (3) is illustrated in Section 5 through simulations and
real data examples followed by concluding remarks.
2 The Bias Change Model and the
GLR
test
The assumption that an anomaly will appear as a bias change from nominal gives
the following hypotheses
H0 : y ∼ p 0 (y),
H1 : y ∼ p 1 (y | ∆) = p 0 (y − ∆) ,
for the unknown bias vector ∆. The expected value of y under
Ep1 [y] = Ep0 [y] + ∆,
(4a)
(4b)
H1
is
(5)
i.e., the mean is changed by ∆. This model is easy to interpret and bias changes
are often considered when detecting anomalies, e.g., in the literature of fault diagnosis (Isermann, 2006). The model describes situations where the data are
shifted in the observation space. The parameter ∆ also carries valuable information about the problem. For instance, if Σ is the density covariance, then ∆ T Σ−1 ∆
measures the significance of the change relative to the density volume, similar to
a signal to noise ratio.
Introducing the notation Yi:j = [y i , · · · , y j ] (j > i), for j − i + 1 independent and
identically distributed (i.i.d.) measurement vectors y i ∈ Rd and let YN , Y1:N ,
the objective is to decide whether a given sample YN belongs to H0 or H1 in (4).
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Data-Driven Anomaly Detection based on a Bias Change
This can be done with a generalized (log-) likelihood ratio (glr),
1
b N (YN ) = max log p N (YN | ∆)
b
sN , log Λ
0
(YN )
pN
∆
= log
1
pN
(YN | b
∆N )
0
(YN )
pN
=
N
X
log
j=1
p 0 (y j − b
∆N )
p 0 (y j )
(6)
,
where b
∆ N is a maximum likelihood (ML) estimate of the unknown bias. Batch
estimation of ∆ is discussed in Section 4.1. The glr is tested based on a threshold
check,
H1
(7)
b
sN ≷ ~,
H0
the above notation means that H0 is chosen if the test statistic b
sN is smaller or
equal to the threshold ~ and otherwise H1 is chosen.
2.1 Unknown change time
The glr quantity used in (6) assumes that the entire batch YN was collected under either H0 or H1 , i.e., the change time is known. In many practical situations,
the change time is unknown. For a batch YN , there are N + 1 possibilities, either
no change was present or a change appeared at any t ∈ {1, . . . , N }. A hypothesis
test for this problem can be defined according to (Basseville and Nikiforov, 1993,
Section 2.6.1),
H0 : y i ∼ p 0 (y), 1 ≤ i ≤ N ,


y ∼ p 0 (y),
1  i
H :

y ∼ p 1 (y | ∆) = p 0 (y − ∆),
i
(8a)
1 ≤ i ≤ t − 1,
t ≤ i ≤ N,
(8b)
where both t and ∆ are unknown in H1 . The resulting glr is based on a joint
maximization of the unknowns as,
e
sN , max max log
1≤t≤N ∆
= max log
1≤t≤N
0
1
p1:t−1
(Y1:t−1 )p t:N
(Yt:N | ∆)
0
pN
(YN )
1
p t:N
(Yt:N | b
∆ t:N )
0
p t:N
(Yt:N )
(9)
= max b
st:N ,
1≤t≤N
and the hypotheses are chosen as in (7) with b
sN replaced by e
sN . An estimate of
the change time is,
b
tN = arg max b
st:N ,
1≤t≤N
(10)
and the estimate for the change is b
∆btN :N . The statistic e
sN requires finding the ML
b
estimate of ∆ t:N for N possible splits t ∈ {1, . . . , N } and evaluation of the related
log-likelihood ratios b
st:N .
2 The Bias Change Model and the glr test
189
2.2 Sequential solution
The formulation in (8) and (9) is a batch approach, i.e., the entire sequence YN is
needed. A sequential approach is however possible by repeating the procedure
for each incoming measurement sequence {y n } (Gustafsson, 2000, Section 3.5.3).
Every time new data y n are received, n estimates b
∆ t:n for t ∈ {1, . . . , n}, are found
and the associated b
st:n are evaluated. The complexity therefore increases with n.
The denominator for b
st:n in (6), i.e., the log-likelihood function for H0 , can be
computed recursively under the i.i.d. assumption. Given the previous value
0
log p t:n−1
(Yt:n−1 ) and y n , it can be updated as,
0
0
log p t:n
(Yt:n ) = log p t:n−1
(Yt:n−1 ) + log p 0 (y n ).
(11)
The numerator for b
st:n , i.e., the log-likelihood function for H1 , must however be
evaluated for the entire Yt:n based on an updated b
∆ t:n which, in general, cannot be found sequentially. The estimate b
∆ t:n requires solution of an optimization problem and is more computationally demanding than the evaluation of the
log-likelihood function for H1 . To reduce complexity, the estimates b
∆ t:n can be
b
found recursively in the data, i.e., ∆ t:n is found based only on the previous value
b
∆ t:n−1 and current measurement y n . Recursive estimation of ∆ is discussed in
Section 4.2.
2.3 Asymptotic performance
Associated to any test is the probability of deciding incorrectly for H0 , denoted Pm ,
and the probability of deciding incorrectly for H1 , denoted Pf . For a glr test they
are given by Pm = Pr b
sN ≤ ~ | H1 and Pf = Pr b
sN > ~ | H0 . While no analytical
solution is available in general, the error probabilities can in principle be found
based on Monte Carlo integration. An alternative is to find Pf and Pm based on
the asymptotic behavior of the glr statistic. The asymptotic behavior of the test
statistic is given by (Mackay, 2003, App. 6A-C),
as.
2b
sN |H0 ∼ Xd2 ,
1 as.
2b
sN |H ∼
Xd′ 2 (λ (∆)) ,
(12a)
T
λ(∆) , ∆ F (0)∆,
(12b)
where ∆ is the true bias change, Xd2 is the chi-square distribution with d degrees
of freedom, Xd′ 2 (λ) is the non-central chi-square with non-centrality parameter λ
and F (0) is the Fisher information matrix for ∆ evaluated at zero. This result is
∆ N tends to the true value ∆.
valid whenever the correct models are used and b
Since the asymptotic behavior of the test statistic does not depend on unknowns
under H0 , a threshold can be found from (12a) for a desired error level Pf′ . An
estimate of Pm can also be computed by using the maximum likelihood estimate b
∆
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Data-Driven Anomaly Detection based on a Bias Change
in (12b). This is summarized as follows


Z∞







2
′
′
~(Pf ) = inf 
~
∈
R
:
X
(s)
ds
≤
P
,
f
d






(13a)
~
~(Pf′ )
Pm (Pf′ )
=
Z
−∞
Xd′ 2 s; λ(b
∆) ds.
(13b)
To apply the glr test for the bias change model, the unknown density p 0 (y) is
needed. In a practical setup, it is often common to introduce assumptions on
the data distribution, the Gaussian model being a common choice. Although the
Gaussian model gives statistical tests that can be conveniently described by sufficient statistics (Van Trees, 2001), it is clear that there will be situations where this
model is a poor description of H0 . In this paper, no assumption is forced about
H0 , instead, all knowledge is considered to be contained in a nominal dataset
YN00 . The next section describes approaches where this dataset is used to find an
approximate model b
p 0 (y) for H0 . Using this approximation, Section 4 describes
methods to find maximum likelihood estimates of the bias change b
∆. The density
and bias estimates define the approximate models in (3) which are tested based
on a glr test assuming it is the true model. The only specification parameter
needed is the desired error level Pf′ to find the threshold ~ as in (13a). The associated error probabilities are found based on the asymptotic expressions in (12) for
the approximate models.
3 Nonparametric Density Estimators
A nominal dataset YN00 with N0 i.i.d. observations from H0 is considered available
and is used to find a nonparametric density estimate b
p 0 (y) for H0 . The density
model will take the form of a finite mixture
X
X
b
p 0 (y) =
πk κ(y; y 0k , h),
πk = 1, πk > 0,
(14)
k∈K
k∈K
where K is an index set with cardinality |K| = K ≤ N0 , κ( · ) is a positive kernel
function that integrates to one. The bandwidth h ∈ Rd is fixed and the weighting coefficients {πk } are found according to the chosen density estimator. Two
nonparametric density estimators are discussed next.
3.1 Kernel density estimator
The first type of estimator considered is a so called kernel density estimator (kde),
or Parzen estimator. The kde based on the nominal dataset YN00 is given by a finite
3 Nonparametric Density Estimators
191
mixture model (14) with
1
πk =
|S (h)|−1/2
N0
κ(y; y 0k , h) = κ S (h)−1/2 y − y 0k ,
K = {1, 2, . . . , N0 },
(15a)
(15b)
where S(h) is a positive definite scaling matrix. The kde model has as many
components as data points and the coefficients {πk } are fixed and identical. As
shown by Parzen (1962); Cacoullos (1966), this estimator is consistent and asymptotically unbiased. The kde method requires specification of the bandwidth h.
There are several approaches reported in the literature for bandwidth selection
(Jones et al., 1996b,a). Here, a diagonal S(h) will be considered with bandwidth
elements chosen using Silverman’s rule of thumb (Silverman, 1986),
S(h) = diag(h)
−1
p
1
4 d+4
bi ,
N0d+4 σ
hi = d+2
(16a)
(16b)
bi is an estimate of the data standard deviation over
for i = {1, . . . , d} and where σ
the ith dimension.
Besides requiring storage of the entire nominal dataset, performing inference
with a kde will become computationally intensive when N0 is large. An alternative is to consider reduced mixture models, with K ≪ N0 components. When the
number of components K is fixed, it is possible to find maximum likelihood estimates for the parameters using, e.g., the em algorithm by Dempster et al. (1977).
A disadvantage with such an approach is that the number of components K must
be pre-specified.
3.2 A sparse density estimator
An alternative will be considered here that requires only few a components to
describe the density estimate. It is based on the generalized cross entropy (gce)
method presented by Botev and Kroese (2011), which does not require specification of K or h. For a dataset YN00 , the estimate is given as
X
b
p 0 (y) =
λ∗k κ(y; y 0k , h∗ ),
(17)
k∈K
∗
with K = {1, . . . , N0 }, λ is a sparse weight vector which, together with the bandwidth h∗ , is given by
(h∗ , λ∗ ) = (h, λ) : 1T λ(h) = 1, λ(h) = arg min λT C(h)λ − λT b
φ(h) . (18a)
λ≥0
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Data-Driven Anomaly Detection based on a Bias Change
The quadratic program (qp) for λ(h) is defined by
1 X
bi (h) =
κ(y 0j ; y 0i , h), i = 1, . . . , N0 ,
φ
N0 − 1
j,i
Z
[C(h)]ij = κ(y; y 0i , h)κ(y; y 0j , h) dy,
(18b)
(18c)
Rd
and C(h) ∈ RN0 ×N0 is positive definite by construction.
This approach is algorithmically similar to the support vector density estimator
by Vapnik and Mukherjee (2000), in which the condition 1T λ(h) = 1 is included
as a constraint in the qp and h is pre-specified. As noted by Botev and Kroese
(2011), the qp in (18a) is closely related to the support vector regression problem
with an ǫ-insensitive error function, see, e.g., (Bishop, 2006, Section 7.1.4), and
most elements in λ∗ will be close to zero.
Computing the estimate
To avoid solving (18a) for a d-dimensional h, a simplification is made which considers a scalar bandwidth h applied to a vector containing an estimate of the
variance along each dimension, i.e.,
h
i
b12 , · · · , σ
bd2 ,
hT = h σ
(19)
bi is an estimate of the standard deviation along the ith dimension. In this
where σ
manner, only one bandwidth parameter needs to be found and different scalings
are allowed for the different dimensions. The resulting problem (18a) is solved
by addressing the nonlinear least squares
2
(20)
h∗ = arg min 1T λ(h) − 1 ,
h
where λ(h) is the solution to the qp (18a) and λ∗ = λ(h∗ ).
To remove small components in λ∗ , a pruning approach is suggested here. Let
λ∗ be ordered as λ∗1 ≤ λ∗2 ≤ . . . λ∗N0 , the ǫ approximation of (17) is written by
replacing K and λ∗k in (17) with Kǫ and πk∗ respectively, where




k


X


λ∗k


∗
∗
X
Kǫ : 
1
≤
k
≤
N
:
λ
≥
ǫ
,
π
,
,
(21)

0
j
k






λ∗
j=1
j
j∈Kǫ
and |Kǫ | = K will typically be much smaller than the number of data samples N0 .
Multivariate Gaussian kernel
The gce method requires solution of [C(h)]ij in (18c), which is not always analytically tractable. For the Gaussian case, i.e., κ(y; y 0k , h) = N (y; y 0k , S(h)), it can be
4 Estimating the Bias Change
193
shown from completion of the squares that
[C(h)]ij = N y 0i ; y 0j , 2S (h) ,
(22)
see the Appendix for a proof. With C(h) found as in (22) with a diagonal S(h)
as in (16a) and h given as (19), the optimal weights λ∗ = λ(h∗ ) are found from
(20) and the approximation (21) is used to present the resulting model as in (14).
Notice that the resulting density estimate is a Gaussian mixture model (gmm).
4 Estimating the Bias Change
For b
p 0 (y) achieved using either the kde or the gce methods, the model for the
alternative hypothesis in (3) can be written as the finite mixture
b
p 1 (y | ∆) = b
p 0 (y − ∆) =
K
X
k=1
πk κk (y − ∆),
(23)
where κk (y) , κ(y; y 0k , h). The objective of this section is to derive batch and
sequential maximum likelihood estimators of ∆ in (23). First, notice that for a
mixture density b
p (y) as given in (14),
Eb
p [y] =
K
X
πk Eκk [y].
k=1
Using this relation with (5), an estimate of ∆ can be computed based on the test
batch YN from the sample estimate as,
N
N
K
X
1 X
1 X
S
b
∆N =
πk Eκk [y].
y n − Eb
[y]
=
y
−
0
p
n
N
N
n=1
n=1
(24)
k=1
Given that Eb
p 0 [y] = Ep 0 [y], i.e., that the mean for the approximate model corresponds to the true mean in the nominal case, the estimate (24) will converge
to the true value. However, for a given sample YN , it does not necessarily maximizes the likelihood function (e.g., if the density is skewed) and an alternative
is needed. It is well known that direct optimization of the likelihood function in
mixture models is problematic (Bishop, 2006, Section 9.2.1). For mixtures, the
em algorithm can be used to compute maximum likelihood estimates.
4.1 Batch estimation using EM
The em algorithm by Dempster et al. (1977), is a two step iterative procedure for
finding maximum likelihood parameter estimates in probabilistic models involving latent (not measured) variables. Let Z and Y denote latent and measured
variables respectively, with joint distribution p(Y , Z | θ) governed by the param-
Paper E
194
Data-Driven Anomaly Detection based on a Bias Change
eter vector θ and let
Q(θ, θ ′ ) ,
Z
log p(Y , Z | θ)p(Z | Y , θ′ ) dZ
(25)
= Eθ′ [log p(Y , Z | θ) | Y ] .
For iterates θ(i) , the expectation (25) is computed for Q(θ, θ(i−1) ) in the E-step.
In the M-step, the resulting Q-function is maximized with respect to θ to update
the iterate θ(i) . The steps are repeated until a convergence criterion is satisfied.
The em algorithm guarantees that the iterates satisfy p(Y | θ(i) ) ≥ p(Y | θ(i−1) )
and therefore they converge to a stationary point of the likelihood function.
E-step for mixture models
As previously noted, the model (23) can be interpreted as a finite mixture model
where the parameter θ = ∆ is common to all mixture components, i.e., it can be
written as
b
p 1 (y | θ) =
K
X
k=1
K
X
πk κk (y | θ),
πk = 1, πk > 0.
(26)
k=1
Following a typical derivation of the em algorithm for mixture models, see, e.g.,
(Bishop, 2006, Section 9.3.1) or Bilmes (1997), a discrete latent variable z with
components zk ∈ {0, 1} is introduced to assign the unique component of the mixture model that generated the data. The latent variable z is assigned according to
the categorical distribution,
p(z) =
K
Y
z
π kk ,
k=1
PK
where p(zk = 1) = πk and k=1 πk = 1 since only one component can be assigned
for each datum. Given the variable z, the conditional is
p(y | z, θ) =
K
Y
k=1
κk (y | θ)zk ,
corresponding to the mixture component that generated the datum. The joint
(complete-data) distribution is
p(y, z | θ) = p(y | z, θ)p(z) =
K
Y
k=1
[πk κk (y | θ)]zk ,
and the marginal over z is
p(y | θ) =
X
z
p(y | z, θ)p(z) =
K
X
k=1
πk κk (y | θ),
4 Estimating the Bias Change
195
which is consistent to the original mixture model (26) and
p(z | y, θ) =
p(y, z | θ)
=
p(y | θ)
K
Y
[πk κk (y | θ)]zk
k=1
K
X
j=1
.
πj κj (y | θ)
For a batch with N independent observations YN , the E-step in the em algorithm
is given by the Q( · , · ) function,
N K


X X

Q(θ, θ′ ) = Eθ′ 
znk log πk κk (y n | θ) YN 
n=1 k=1
=
N X
K
X
Eθ′ [znk | YN ] log πk κk (y n | θ)
=
N X
K
X
ζnk (θ′ ) log πk κk (y n | θ),
n=1 k=1
n=1 k=1
(27)
where znk assigns the latent variable for the nth data point and kth component in
the mixture and ζnk (θ′ ) is its expected value under p(z | y, θ), given by
ζnk (θ′ ) , Eθ′ [znk | YN ] =
=
X
zn
X
ZN
znk p N (ZN | YN , θ′ ) =
πk κk (y n | θ′ )
znk p(z n | y n , θ′ ) = P K
j=1
πj κj (y n | θ′ )
X
ZN
znk
N
Y
j=1
p(z j | y j , θ′ )
.
(28)
where the second to last step follows since the p(z | y, θ) factorizes over n.
M-step for a bias change in a Gaussian Mixture model
The solution to the M-step depends on the form of the kernel function and on
how the unknown parameters enter this function. Explicit solutions are given
next for the Gaussian mixture model (gmm) based on YN and with
θ = ∆,
κk (y | θ) = κk (y − ∆) = N (y − ∆; y 0k , S).
(29)
The M-step can be found explicitly by finding the solution to ∂ Q(∆, ∆ ′ ) = 0. This
∂∆
gradient is given by
"
#
N X
K
X
∂
′
ζnk (∆ )
log κk (y n − ∆) ,
∂∆
n=1 k=1
196
Paper E
Data-Driven Anomaly Detection based on a Bias Change
where the term in brackets simplifies to
h
iT
h
i
∂
−1/2 y n − y 0k − ∆ S −1 y n − y 0k − ∆
∂∆
o
∂ n T −1
=
∆ S (y n − y 0k ) − 12 ∆ T S −1 ∆ = S −1 (y n − y 0k ) − S −1 ∆,
∂∆
giving the solution
PN PK
′
0
N K
1 XX
n=1 k=1 ζ nk (∆ ) y n − y k
=
∆=
ζnk (∆′ ) y n − y 0k ,
PN PK
′
N
n=1 k=1 ζ nk (∆ )
(30)
n=1 k=1
P
′
(i)
where the last step follows since K
prok=1 ζ nk (∆ ) = 1. The resulting iterates ∆
duced from the em algorithm are given in Algorithm 1 for a convergence criterion
based on k∆(i) − ∆ (i−1) k22 . The algorithm can be initialized using (24), which, for
the gmm, gives
∆ (0) =
N
K
X
1 X
πk y 0k .
yn −
N
n=1
(31)
k=1
Algorithm 1 Batch em for a bias change in a gmm
Set i = 1, ∆ (i−1) as (31) and ǫ > 0.
repeat
E-Step: compute ζnk (∆(i−1) ) as in (28)
M-step: compute ∆ (i) according to (30)
until k∆ (i) − ∆(i−1) k22 ≤ ǫ
return b
∆ N = ∆ (i) {Return the estimate}
4.2 Sequential estimation using stochastic approximation
To evaluate the E-step in the em algorithm, all measurements in YN must be available and the em algorithm is therefore a batch approach. A recursive version of
em was suggested by Cappé and Moulines (2009), based on a stochastic approximation of the E-step according to,
en (θ) = γn E b [log p(y, z | θ) | y ] + (1 − γn )Q
en−1 (θ),
Q
n
θ n−1
(32)
where γn is a forgetting factor, controlling the adaptation rate to incoming measurements. The M-step is unchanged and the estimate b
θ n is taken as the maxen -function. Consistency and convergence rates for the estimator
imum of the Q
(32) are studied in Cappé and Moulines (2009). For consistency, γn must be chosen such that
∞
∞
X
X
0 < γn < 1,
γj = ∞,
γj2 < ∞.
j=1
j=1
4 Estimating the Bias Change
197
To satisfy these conditions, the authors suggest the use of
γn = γ0 n−ρ ,
for γ0 ∈ (0, 1) and ρ ∈ ( 12 , 1]. The particular choice γ0 = ρ = 1 is equivalent to the
recursion of Equation 12 given in Titterington (1984).
For mixture models, (32) follows as
en (θ) = γn
Q
K
X
k=1
en−1 (θ),
ζnk (b
θ n−1 ) log πk κk (y n | θ) + (1 − γn )Q
(33)
where ζnk ( · ) is evaluated at the previous estimate b
θ n−1 . For a gmm as in (29), a
e0 (∆) = − 1 ∆ T S −1 ∆
recursive solution to the M-step can be found. Starting with Q
2
e1 (∆), Q
e2 (∆), . . . , Q
en (∆), for a
and b
∆ 0 = 0 (no change), direct maximization of Q
sequence {y n } gives
b
∆ n = γn
K
X
ζnk (b
∆ n−1 )(y n − y 0k ) + (1 − γn )b
∆ n−1 ,
(34)
b
ζnk (b
∆ t:n−1 )(y n − y 0k ) + (1 − γe
n )∆ t:n−1 ,
(35)
k=1
see the Appendix for a proof. Similarly, to find b
∆ t:n sequentially as described in
Section 2.2, the recursion is
b
∆ t:n = γe
n
K
X
k=1
where e
n = n − t + 1. Recursion (35) gives rise to Algorithm 2, which produces an
estimate b
∆ t:n recursively at each new measurement y n . The estimate b
∆ t:n is used
in sequential tests to compute the glr statistic e
sn in (9) where b
∆ t:n needs to be
found for t ∈ {1, . . . , n}. That is, for each incoming data y n , n bias estimates are
updated.
Algorithm 2 Sequential em for a bias change in a gmm
Set n = t, b
∆ t:n−1 = 0, γ0 ∈ (0, 1) and ρ ∈ ( 21 , 1]
for all incoming y n , n ≥ t do
E-Step: compute ζnk (b
∆ t:n−1 ) as in (28) and set e
n = n − t + 1.
M-step: set γñ = γ0 ñ−ρ and compute b
∆ t:n as in (35)
end for
Notice that the computational complexities of Algorithms 1 and 2 are directly
proportional to the number of kernels K. Therefore, the use of sparse models,
such as the ones achieved by the gce method, give the advantage of a reduced
computation load.
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Data-Driven Anomaly Detection based on a Bias Change
5 Illustrative Examples
5.1 Simulation study
The performance of statistical tests based on the approximated models (3) will depend on how close they are to true models (1). As noticed by Eguchi and Copas
(2006), the power that is lost over all thresholds in a Neyman-Pearson test, i.e.,
based on a likelihood ratio statistic, when the true density for H1 , p 1 (y), is misspecified by b
p 1 (y) is given by the relative entropy (Kullback-Leibler divergence)
between the true and approximate densities, i.e.,
Z∞
−∞
Pr s (y) = log
!
!
b
p 1 (y)
p 1 (y)
H1 − Pr b
H1 d~
(y)
>
~
s
=
log
>
~,
p 0 (y)
p 0 (y)
Z
p 1 (y)
p 1 , p 1 (y) log 1
dy.
= DKL p 1 ||b
b
p (y)
0
A similar result is also
given
p 0 (y), where the overall
in case H is misspecified by b
0
0
power loss is DKL p ||b
p . To illustrate the effects of different model approximations and potential gains of the suggested ideas, different approximations are
evaluated for a selection of simulation problems. A total of seven problems
are considered as described in Table 1. The densities for cases 1-6 were taken
from Marron and Wand (1992); Botev et al. (2010). For each problem, a nominal
dataset YN00 is generated with N0 = 200 samples. Three different approximations
of p 0 (y) are considered based on the nominal dataset YN00 :
• a Gaussian with parameters given from the standard maximum likelihood
equations,
• a nonparametric model given by the kde with a Gaussian kernel and bandwidth found using (16),
• a nonparametric model given by the gce with a Gaussian kernel and an
ǫ = 10−8 approximation.
The true density for H1 is taken as p 0 (y − ∆) where ∆ = 2 for all cases. From
H1 , a test dataset YN with N = 50 samples is generated. The test data YN is used
for the estimation of ∆ based on the three different models. For the Gaussian
model, a maximum likelihood estimator is used, i.e., the estimate is the difference
Gauss
between the dataset averages b
∆N
= ȲN − ȲN00 . For the kde and gce models, the
parameter is estimated using the batch em solution given in Algorithm 1 with
initial value given by (31).
To analyze the effects of using the approximated
models
in statistical tests, the
overall power losses given by DKL p 0 ||b
p 0 and DKL p 1 ||b
p 1 are computed, as well
as the mean squared error (mse) for the achieved estimates b
∆ N . The achieved
values are shown in Table 1. As can be seen, the gce estimates consistently give
smaller values for the relative entropies and thus give glr statistics that are closer
to the optimal (achieved when the true model is used). This is the case even
5 Illustrative Examples
199
though the achieved estimates b
∆ N do not always give the smallest mse. The overall better performance of the gce model can be motivated from its information
theoretic derivation (see Botev and Kroese (2011)). Note that the performance of
the kde models could possibly be improved if a different bandwidth selection
method was used. The resulting number of components |Kǫ | for the gce estimates are 12, 12, 8, 5, 5, 14, 30 for cases 1-7 respectively. Compared to the kde,
the reduced number of components for the gce estimates considerably reduces
the amount of computations needed to find the bias estimates.
5.2 Batch detection of an increase in eruptions
The Old Faithful geyser dataset (Azzalini and Bowman, 1990) is considered here
to illustrate the methods for the batch multivariate case with known change time.
The dataset contains 272 measurements with d = 2 dimensions representing the
registered length of the geyser’s eruptions and the time between eruptions (both
in minutes). A fraction N0 = 222 of the measurements are used to estimate a
density for the nominal model b
p 0 (y). As before, three different models are considered: a Gaussian model and nonparametric models given by the kde and gce
methods with Gaussian kernels.
The measurements YN00 are shown in Figure 1a together with contour lines for the
density models. The components chosen for the gce model are also shown in Figure 1a with a colormap relating to the weights π∗ . With K = 32, the gce requires
86% less data to represent the density compared to the kde. The gce is also
richer in details and with a tighter support compared to the kde and Gaussian
models.
A bias change is considered to illustrate the situation where the length of eruptions is increased by half a minute and the interval between them is reduced by 2
minutes, i.e., ∆ = [0.5, −2]T . These values are added to the N = 50 remaining measurements, which can be seen in Figure 1a. Using these abnormal measurements
YN , ∆ is estimated for the three different models in the same manner as discussed
in Section 5.1 but the em Algorithm 1 is initialized with ∆ (0) = [0, 0]T for a comparison. Notice the large bias for the estimate given by the Gaussian model. The
iterates ∆ (i) are shown in Figure 1b as a function of iterations. Due to the sparsity
of the gce, b
∆ N is computed 40 times faster compared to the estimate given by the
kde. After convergence of the iterates, the glr statistic b
sN is computed for the
different models, the values are 9.18, 21.71 and 83.71 for the Gaussian, kde and
gce models, respectively. Based on the asymptotic expression (13a), a threshold
~(0.01) = 4.60 is found. All tests can detect the change, although the one based on
the gce gives a much clearer response.
5.3 Sequential detection of an increase of wear in an industrial
robot joint
By processing torque measurements collected from an industrial robot joint, a
scalar quantity y is generated to infer the mechanical condition of the joint gearbox (Bittencourt et al., 2014). The generated quantity y is positive and remains
Data-Driven Anomaly Detection based on a Bias Change
Paper E
200
Case
1. Skewed
2. Kurtotic
3. Outlier
4. Bimodal
5. Claw
6. LogN
7. 2-dim
− 3, ( 32 )
p 0 (y)
2k
3k−1
2k
87.26
87.18
44.89
58.34
81.66
52.53
66.73
Gauss
43.67
66.00
45.28
56.79
60.43
47.94
55.96
kde
8.32
45.61
44.11
55.29
57.96
43.31
43.49
gce
417.0
39.95
0.04
13.90
2.52
3.09
0.01
Gauss
17.06
64.80
6.94
0.02
0.90
14.24
19.31
kde
148.77
3.95
3.49
21.04
0.82
33.46
6.76
gce
88.98
90.39
44.77
57.67
81.50
52.22
66.71
Gauss
43.87
68.93
45.61
56.71
60.37
48.56
55.01
kde
14.05
49.06
43.83
56.53
57.83
45.83
42.38
gce
Table 1: Comparative study of different model approximations for a selection of problems.
DKL p 0 ||b
p 0 [ 10−2 ]
mse [ 10−3 ]
DKL p 1 ||b
p 1 [ 10−2 ]
1
k=0 8 N
1
k=0 10 N
P4
log N (0, 1)
[1, 2]T , diag 2, 21 +
[−3, −5]T , I
(0, 1) +
2
1
1
3 N (0, 1) + 3 N 0, 100
1
9
1
10 N (0, 1) + 10 N 0, 100
1
4
1
4
2 N −1, 9 + 2 N 1, 9
k−2 1
2 , 100
P7
1
2N
1
2N
1
2N
5 Illustrative Examples
inter-eruption [min]
Y0N0
201
Gaussian
YN
kde
π∗
gce
100
80
60
40
1
2
3
5
4
6
duration [min]
(a) Estimates of p 0 (y) and measurements.
inter-eruption [min]
duration [min]
0
true
Gaussian
kde
gce
0.5
−2
0
0
5
10
iteration
15
20
(b) Estimates of eruption duration (thick) and inter-eruption
interval (thin). Notice the different axes.
Figure 1: glr test for detection of eruptions increase in a geyser dataset.
Notice how the test measurements YN in Figure 1a overlap with the support
for the nominal models. Despite this, a detection is achieved with any of the
models, although the test based on the gce gives a clearer response.
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202
Data-Driven Anomaly Detection based on a Bias Change
80
hist
Gaussian
kde
gce
π∗
b
p 0 (y)
60
40
20
0
−0.01
0
0.01
0.02
y
0.03
0.05
0.04
(a) Estimates of p 0 (y).
Gaussian
kde
gce
yn
0.2
b
∆t̂n :n
0.15
0.1
0.05
0
2
4
6
8
10 12 14 16 18 20 22
n
(b) Test data sequence yn and estimates b
∆b
t :n .
n
15
e
sn
10
1
Gaussian
kde
gce
~
Pm (0.01)
20
0.5
5
0
0
5
10
n
15
20
(c) glr statistic e
sn (thick) and Pm (0.01) (thin). Notice the different axes.
Figure 2: glr test for detection of abnormalities in the gearbox of a robot
joint. Notice the false alarms triggered with the use of a Gaussian model.
6 Conclusions and Future Work
203
close to zero under normal conditions, deviating otherwise to indicate an anomaly.
The data processing used in the generation of y makes it difficult to determine
its distribution function. From this application, it is however possible to collect
nominal measurements before the application of the test. Based on N0 = 66 nominal samples, the three models from Section 5.1 are considered. The resulting
models and histogram of YN00 can be seen in Figure 2a. The distribution of the
measurements is multimodal and asymmetric, which makes the Gaussian model
a poor representation with its mean falling in a region of the support with little
data. The kde estimate captures the asymmetry in the data, but not the multiple modes and presents a wide support; perhaps a more sophisticated choice of
bandwidth would have improved the kde estimate. The gce estimate uses only
four components, a reduction of 96% compared to the kde. It also captures the
multiple modes, asymmetry and has a tighter support.
Using these models, the objective is to detect a wear fault appearing around t =
16 in a sequence {yn } with 1 ≤ n ≤ 22. The change time is unknown and a
sequential solution is sought as described in Sections. 2.1 and 2.2. To reduce
the computational complexity, recursive maximum likelihood estimates b
∆t:n are
found for 1 ≤ t ≤ n. For the Gaussian model, the standard maximum likelihood
estimate is used. Algorithm 2 is used for the kde and gce models with γ0 = 0.6
and ρ = 1. The data {yn } and the different estimates b
∆btn :n , with b
tn given in (10),
are shown in Figure 2b. Up to n = 16, yn has values smaller than the mean for
the Gaussian model making the estimate deviate towards negative values for this
model.
The resulting models are used to find the glr statistic e
sn as given in (9), these are
shown in Figure 2c together with the threshold ~(0.01) = 3.32 found according to
(13a). The Gaussian model generates false alarms from n = 7 and the kde and gce
based tests detect a change from n = 16. The error probabilities Pm (0.01) given
by (13b) are found based on numerical evaluation of the Fisher information, the
values are also shown in Figure 2c as a function of n. As can be seen, there is a
sharp decay of Pm for the Gaussian and gce models. The Gaussian model gives
smaller values of Pm before the change. The value for Pm achieved with the kde
decays more slowly compared to the others, with a value of 0.73 at n = 16.
In this application, an early detection is very important to allow for condition
based maintenance, giving enough time to perform maintenance. To decide for
maintenance actions, it is also critical to have few false alarms and that the detection error Pm can be used to support service decisions. In this application,
the test obtained using the gce model presented the best compromise for these
requirements.
6 Conclusions and Future Work
This paper proposed a two step approach for anomaly detection using a bias
change model and glr tests. In the first step, a model for the normality is found
based on a nominal dataset. Nonparametric density estimates are used which
Paper E
204
Data-Driven Anomaly Detection based on a Bias Change
give high flexibility since specification of a density function is not needed. In the
second step, maximum likelihood estimates of a bias change are computed using
the em algorithm. The use of a sparse density model can considerably reduce the
computations needed for the estimators. The density model and bias change estimate are then used in glr tests to decide whether an abnormality is present or
not. Both batch and sequential cases are considered and the approaches only
require availability of nominal data and minimal/meaningful specification in
terms of a desired probability of false alarm (to find a threshold). Using available asymptotic expressions for the glr statistic, it is also possible to give estimates of the uncertainties associated with the decision, which are important to
support higher level decisions. The approaches were illustrated in simulations
and real data examples including the detection of an increase of eruptions in a
geyser and of a wear fault in an industrial robot joint. The results achieved show
clear improvements compared to tests based on a Gaussian assumption.
Currently, the decision errors are estimated based on asymptotic expressions
which may differ for a finite number of measurements. In this direction, it would
be interesting to study approaches to provide estimates for the finite sample behavior of the error probabilities. This could possibly lead to the derivation of
adaptive thresholds and more accurate error estimates.
Appendix
Proof of (22)
Let [C]ij ,
R
Rd
N (y; y i , S)N (y; y i , S) dy, P , S −1 and c = (2π)−d/2 |S|−1/2 then
Z
o
1n
− [y −y i ]T P[y −y i ]+[y −y j ]T P[y −y j ]
[C]ij = c e 2
dy.
Using weighted inner product notation, the term in curly brackets is written as
hy − y i , y − y i iP + hy − y j , y − y j iP and simplifies to
2hy, yiP − 2hy, y i + y j iP + hy i , y i iP + hy j , y j iP
+ 12 hy i + y j , y i + y j iP − 21 hy i + y j , y i + y j iP
y +y
y +y
= hy − i 2 j , y − i 2 j i2P + hy i − y j , y i − y j iP/2 .
Rearranging c and taking the integral gives the result
Z
y +y
[C]ij = N (y i ; y j , 2S) N (y; i 2 j , S/2) dy = N (y i ; y j , 2S).
6 Conclusions and Future Work
205
Proof of (34)
We show the results for n = 1 and n = 2, the remaining follows by induction. Let
e0 (∆) = − 1 ∆ T P∆ = − 1 k∆k2 , b
P , S −1 , Q
P ∆ 0 = 0, and (29), then (33) gives
2
2
e1 (∆) ∝ − 1 γ1
Q
2
∂ e
Q (∆) = γ1
∂∆ 1
K
X
k=1
K
X
k=1
ζ1k (b
∆ 0 )k(y 1 − y k ) − ∆k2P − 21 (1 − γ1 )k∆k2P
ζ1k (b
∆ 0 )P[(y 1 − y k ) − ∆)] − (1 − γ1 )P∆
P
b
and therefore b
∆ 1 = γ1 K
k=1 ζ1k (∆ 0 )(y 1 − y k ). Similarly, for n = 2
K
X
h
i
∂ e
Q2 (∆) = γ2
ζ2k (b
∆ 1 )P (y 2 − y k ) − ∆)
∂∆
k=1


K
h
i
 X


b
+ (1 − γ2 ) γ1
ζ1k (∆ 0 )P (y 1 − y k ) − ∆) − (1 − γ1 )P∆ 
k=1
which gives b
∆ 2 = γ2
PK
k=1
ζ2k (b
∆ 1 )(y 2 − y 0k ) + (1 − γ2 )b
∆1 .
206
Paper E
Data-Driven Anomaly Detection based on a Bias Change
Bibliography
D. Agarwal. Detecting anomalies in cross-classified streams: a Bayesian approach.
Knowledge and Information Systems, 11(1):29–44, 2007.
A. Azzalini and A. W. Bowman. A look at some data on the Old Faithful Geyser.
Journal of the Royal Statistical Society. Series C (Applied Statistics), 39(3):357–
365, 1990.
M. Basseville and I. V. Nikiforov. Detection of abrupt changes: theory and application. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1993.
J. A. Bilmes. A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical
Report TR-97-021, International Computer Science Institute, Berkeley, California, May 1997.
C. M. Bishop. Pattern Recognition and Machine Learning. Springer, New York,
USA, 1st edition, 2006.
A. C. Bittencourt, K. Saarinen, S. Sander-Tavallaey, S. Gunnarsson, and M. Norrlöf. A data-driven approach to diagnostics of repetitive processes in the distribution domain – Applications to gearbox diagnostics in industrial robots and
rotating machines. Mechatronics, -(0):–, 2014. available online.
Z. Botev and D. Kroese. The generalized cross entropy method, with applications
to probability density estimation. Methodology and Computing in Applied
Probability, 13:1–27, 2011.
Z. I. Botev, J. F. Grotowski, and D. P. Kroese. Kernel density estimation via diffusion. The Annals of Statistics, 38(5):2916–2957, 2010.
T. Cacoullos. Estimation of a multivariate density. Annals of the Institute of
Statistical Mathematics, 18:179–189, 1966.
O. Cappé and E. Moulines. On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B (Statistical
Methodology), 71(3):593–613, 2009.
V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM
Computing Surveys (CSUR), 41(3):15, 2009.
A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series
B (Methodological), 39(1):1–38, 1977.
M. Desforges, P. Jacob, and J. Cooper. Applications of probability density estimation to the detection of abnormal conditions in engineering. Proceedings of the
Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering
Science, 212(8):687–703, 1998.
Bibliography
207
L. Devroye and G. L. Wise. Detection of abnormal behavior via nonparametric
estimation of the support. SIAM Journal on Applied Mathematics, 38(3):480–
488, 1980.
S. Eguchi and J. Copas. Interpreting Kullback-Leibler divergence with the
Neyman-Pearson lemma. Journal of Multivariate Analysis, 97(9):2034–2040,
2006.
F. Gustafsson. Adaptive Filtering and Change Detection. Wiley, October 2000.
R. Isermann. Fault-Diagnosis Systems - An Introduction from Fault Detection to
Fault Tolerance. Springer, 1st edition, 2006.
C. Jones, J. Marron, and S. Sheather. Progress in data-based bandwidth selection
for kernel density estimation. Computational Statistics, (11):337–381, 1996a.
M. C. Jones, J. S. Marron, and S. J. Sheather. A brief survey of bandwidth selection
for density estimation. Journal of the American Statistical Association, 91(433):
401–407, 1996b.
D. J. C. Mackay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 1st edition, June 2003.
J. S. Marron and M. P. Wand. Exact mean integrated squared error. The Annals
of Statistics, 20(2):712–736, 1992.
J. Neyman and E. S. Pearson. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London.
Series A, Containing Papers of a Mathematical or Physical Character, 231:289–
337, 1933.
E. Parzen. On estimation of a probability density function and mode. The Annals
of Mathematical Statistics, 33(3):1065–1076, 1962.
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural computation,
13(7):1443–1471, 2001.
B. W. Silverman. Density estimation for statistics and data analysis, volume 26.
Chapman & Hall/CRC, 1986.
D. M. Titterington. Recursive parameter estimation using incomplete data. Journal of the Royal Statistical Society. Series B (Methodological), 46(2):257–267,
1984.
H. L. Van Trees. Detection, Estimation and Modulation Theory, Part I. Wiley,
New York, 1st edition, 2001.
V. N. Vapnik and S. Mukherjee. Support vector method for multivariate density estimation. In Advances in Neural Information Processing Systems (NIPS),
pages 659–665. MIT Press, 2000.
208
Paper E
Data-Driven Anomaly Detection based on a Bias Change
A. Wald. Sequential tests of statistical hypotheses. The Annals of Mathematical
Statistics, 16(2):117–186, 1945.
D.-Y. Yeung and C. Chow. Parzen-window network intrusion detectors. In Proceedings of the 16th International Conference on Pattern Recognition, volume 4, pages 385–388, Quebec, Canada, 2002. IEEE.
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