Vibration Monitoring of Rotating Machines Using MEMS

Vibration Monitoring of Rotating Machines Using MEMS
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
Vibration Monitoring of Rotating Machines Using
MEMS Accelerometer
Subimal Bikash Chaudhury1, Mainak Sengupta2, Kaushik Mukherjee2
1 Automation Division, Tata Steel, Jamshedpur, India,
2 Electrical Engineering, Indian Institute of Engineering Science & Technology, Shibpur, India
Abstract: Heavy industries face major problems since different types of mechanical failures can originate in rotating machines.
Analytical approaches have demonstrated that vibration monitoring has tremendous potential in detecting and localizing defects in the
machines. There are different technologies available for vibration sensing. Though MEMS accelerometer is slowly becoming an
alternate method for vibration monitoring of rotating machines, yet it has not been fully explored for a much wider application base.
This paper proposes the basic design for the development of a low cost MEMS accelerometer based vibration sensor by integrating the
basic sensor and intelligence of vibration analysis, together. This module can easily be deployed for different rotating machines for
vibration monitoring. Sensitivity of the sensor, effectiveness of the proposed intelligence in signal processing and their performance are
tested for a 7.5KW, 3φ, 440V, 4 pole squirrel-cage induction motor. The experiments are carried out to check the ability to detect the fault
frequency peaks under different fault combinations. The results presented here are found to be highly promising.
Keywords: Rotating Machine, MEMS accelerometer, vibration sensor, autocorrelation function, fault diagnosis, signal processing
1. Introduction
The fault diagnosis and prognosis of a rotating machine using
vibration pattern analysis, is one of the efficient and most
successful techniques [1-3]. Analytical and practical understanding of machine vibration is well defined in literatures
[2-5]. The machine vibration behavior, in time and frequency
domain, forms the basis for monitoring of rotating machines
[6]. It has gained enormous importance in the last decade, as
machine vibration response is sensitive to any small change
in operating condition or mechanical structural [2,4].
are emerging technologies. Fig.-1 presents different physical
parameters for vibration sensing, sensors adopted for sensing,
and technologies available for vibration monitoring.
Out of the different technologies, MEMS accelerometers are
projected to hold great promise for the future of smart vibration sensing [10]. Number of research studies exist in the
literature [10,11] about MEMS accelerometer construction,
mounting considerations, measurement principle and performance evaluation. These MEMS based accelerometers are
emerging as an alternate method of sensing the vibration in
rotating machines. Although MEMS technology is widely
applied in biomedical, automotive and consumer sectors, yet
there is a lack of rigorous investigation of MEMS accelerometers performance in vibration measurements under different
operating and fault conditions of rotating machines.
The main objective of this work is to investigate the feasibility of MEMS accelerometer for vibration monitoring of mechanical equipments. The complete vibration transducer design aspects are detailed here. The influence of vibration
noise in rotating machines is investigated, particularly when
useful signal becomes hard to obtain. An auto-correlation
based noise cancellation algorithm and adaptive rule-based
filter are developed to improve the characteristic signal detection capability in the acceleration signal.
Figure 1: Overview of different vibration sensors and technologies used for vibration monitoring
The vibration analysis demands appropriate vibration transducers. Vibration measurement can be done by measuring
displacement, velocity, acceleration, acoustic, magnetic, optical etc. of specific points, of otherwise static structure. These
parameters can be measured with different types of sensing
devices based on different principles. Several techniques,
mainly based on capacitive/piezoelectric accelerometers and
acoustic are available commercially. Optical [7,8] and GMR
(Giant Magnetoresistance) [9] based vibration measurement
Paper ID: J2013358
To experimentally verify the validity of the deign aspects of
vibration transducer and the proposed signal processing technique, a series of laboratory tests were conducted. The test
rig comprised mainly of the test motor with gear arrangement
for load transfer and dynamometer. Different conditions were
emulated for the motor and load arrangement, corresponding
to different faults like shaft misalignment, motor misalignment, loose foundation, eccentric loading conditions etc.
This paper is organized as follows. In Section II, the basic
principle of MEMS sensor is discussed. The design aspects
of MEMS based vibration transducer and the complete development are discussed in Sections III and IV respectively.
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
5 of 11
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
The experimental results are presented in Section V. Finally
conclusions are given in Section VI.
2. MEMS Accelerometer - Fundamentals
MEMS accelerometers are based on two sensing principles
(1) piezoelectric effect – in which microscopic crystal gets
stressed by accelerative forces, (2) capacitive type –where the
capacitance changes between two fingers due to microscopic
seismic mass movement [10-13]. In either case a voltage is
generated which is the measure of force or displacement.
Capacitive type MEMS accelerometers [14, 15] are widely
used since they need minimal processing power, produce a
large output signal, have excellent sensitivity and are intrinsically insensitive to temperature variations. The sectional
view and the simplified lumped parameter model for a capacitive type MEMS accelerometer is shown in Fig.-2(a,b).
mass ‘m’ (direction of arrow mark) and is measured using the
change in the capacitance (ΔC) between the anchor and the
electrodes. The force acting on the proof mass (= m x a) is
balanced by the restoring force ‘= ks x x’ generated by the
spring. Where ks is the spring constant.
∴a =
ks
x
m
(2)
Under no deflection condition:
C1 = C 2 = C0
(3)
Under deflection:
(4)
1
d+x
1
C2 = C0 − ΔC = ε 0
d−x
C1 = C0 + ΔC = ε 0
Considering
x < d,
∴ x 2 << d 2
The voltage balance (based on charge conservation) for one
set of capacitor bank can be considered as:
Vx = V0
C2 − C1
C 2 + C1
Vx ΔC x
=
=
V0 C0 d
∴x = d
ΔC
C0
where, Vx , V0 are sensed and applied voltages, respectively.
(a) Sectional view of MEMS accelerometer
Solving (2), (6) and (7), acceleration can be defined as
a=
ks
Vx
mV0
(8)
cons tan t
Equ.-(8) provides the basis of acceleration measurement.
3. Design Consideration
The bandwidth, noise characteristics and sensitivity of
MEMS sensors are important parameters for selecting the
MEMS and designing the circuit. Some of the design parameters are discussed here.
(b) Lumped parameter model
Figure 2: Capacitive type MEMS accelerometer
This type of sensor works on the principle of capacitance
variation between a set of sensing electrodes and reference
(fixed) electrodes. The capacitance between two parallel
plates with ‘A’, as the area of each plate, and ‘d’, as the separation between the plates (Fig.-2b), can be given as:
A
C = ε 0ε r
(1)
D
Where, ε0 is the permittivity of free space and εr is the dielectric constant or relative permittivity of the insulator used. The
acceleration is inferred from the displacement (x) of the proof
Paper ID: J2013358
3.1 Bandwidth selection
The bandwidth of MEMS accelerometer gives the maximum
input frequency for which the sensor will respond effectively.
During frequency analysis of vibration signal, the appearance
of prominent periodic frequency components is an indication
of the machine condition [7, 8]. These frequencies are generally related to the rotational speed of various parts of the machine. For example if the rotational frequency appears as f r ,
then the coupling misalignment will change the amplitude
of f r significantly, whereas, 2 × f r will change only in case
of misaligned shafts. Similarly, gear mesh frequency
f = n × f where, n is number of gear teeth and f is gear
Z
g
g
rotating speed. These frequencies of interest are the deciding
factors for selecting the required bandwidth of the sensor.
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
6 of 11
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
Here, the design of MEMS accelerometer as vibration sensor
is restricted to fault frequency identification for misaligned
rotating shaft with speed ranging from few RPM to
3000RPM (50Hz). Hence, a device with low bandwidth (0500Hz) is selected.
3.2 Signal pre-processing
Most of the MEMS accelerometer outputs and bias outputs
(i.e. output at g = 0) are ratiometric. The DC-component
which appears in the signals is either an artifact of the sensors
and electronics associated with the hardware or it may be due
to the low-speed operation of the motor. It is essential for the
DC-component to be removed before conducting any frequency domain analysis. Moreover, to get the best resolution
of ADC it is recommended to remove the bias voltage and
amplify the signal with proper gain. It is proposed to carry
out same using level-shifter with gain amplifier using instrument amplifier. Fig.- 3 shows the basic schematic of the amplifier considered for bias voltage removal and signal amplification.
subsequent paragraphs.
The inherent noise of MEMS has 1 f type noise characteristics at low frequencies and white Gaussian noise at high frequencies [17,18]. This is random in smaller time frames of
the vibration signal with uncorrelated nature. Vibration signal
from other sources can also be random but for longer time
frame, non-persistent and at higher frequency band-width.
The nature of this noise signal may be periodic or aperiodic. The
aperiodic random signal appears as noise floor, which may vary
depending on their randomness and amplitude. In the present
design, the removal all of these unwanted signals is done via
multiple steps- using spectral averaging, one-sided autocorrelation function and using rule based filter (using Spectral
Subtraction).
Auto‐
correlation
FFT ‐3.3V
x̂[k ]
R3
+3.3V R2
Digitize
‐3.3V
Signal
+3.3V
x(t )
(
~
x[k ] = x̂[k ] − X 0F
no
‐3.3V
Figure 3: Schematic for signal preprocessing.
Further, a new concept is proposed to remove the residual
DC component. The DC-component in the signal can be obtained as the amplitude of zero frequency component of the
spectral decomposition (FFT) of continuous time series sensor signal x(t) . This is expressed here as,
~
F
X DC
= X 0F
where,
F represents the DC-component in digitized signal
X DC
~
X 0F is the zero frequency component obtained from FFT
digitized frame number.
F
X filter [k ] = x[k ]
)
O/P
R1
ℜk [k ] > ℜk [i ]
&
X k [k ] < X k [i ]
X filter [k ] = 0
DC Removal
R5
C1
FFT X [k ]
FFT
Rk(k)
yes
R6
R4
I/P
X F [k ]
Remove Lag Component & mask initial data
X filter [k ]
..IFFT ..
x filter (t )
Figure 4: Annotation of different steps involved in denoising of vibration signal using Auto-correlation function
One of the important contributions of this paper is to propose, develop and implement logic for random noise removal
using auto-correlation function. This is illustrated in Fig.-4.
3.4 Mounting requirement and orientation
The mounting and orientation of the basic MEMS sensor is
very important. The orientation of MEMS is critical, since
the measured acceleration is sensitive to a particular direction. The direction of acceleration which needs to be measured is dependent on the type of fault causing the vibration.
Each digitized frame x̂[k ] captured from continuous time
series signal x(t ) is corrected by subtracting the DCcomponent from the digitized frame. The reconstructed frame
is:
(
~
X̂ F = x̂[k ] − X 0F
)
3.3 Noise consideration
The inherent noise of the accelerometer and the large amount
of vibration noise which often originates in the floor area of
the installation, need to be considered for recovering the desired signal from the accelerometer signal. The design aspect
for removing both these types of noises is discussed in the
Paper ID: J2013358
Figure 5: Schematic of the probable location for sensor fixing. (1) Y axis is along the perpendicular direction of gravitational force, (2) Y axis is along the gravitational force
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
7 of 11
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
For example, a shaft misalignment will have more prominent
axial vibration, whereas radial vibration measurement will be
more salient in the case of an imbalance in the machine. Two
possible mounting locations ‘a’ and ‘b’ are indicated in Fig.5. Although both these positions can be considered, the position ‘b’ is preferable since at this position the radial component will be more prominent and will provide a direct indication of the machine alignment. Here, in either position, x-axis
of the MEMS is oriented towards the machine shaft axis (i.e.
axial direction of motor). It is better to have the alignment of
x-axis of the MEMS along the shaft axis of the motor, which
will in turn give more accurate measurement.
4. Development of Vibration Transducer
(marked as ‘a’). A rectangular groove is recessed on the inner
surface of this block, to provide proper mounting base for the
basic MEMS sensor.
Part (2) is the main body in which the preprocessing signal
amplifier card is fixed (marked as ‘b’). Part (1) and part (c)
get attached to the body part (b) through male-female type
threading arrangement. Part (a) has male tapered threading
whereas part (c) has female tapered threading. This arrangement for coupling of different parts will give an easy access
to each component during maintenance. For supply in and for
signal out, lightweight threaded - 5pin circular connector
(DIN 45322) is used. This connector is fitted in the assembly
part (3) and is marked as (c). The complete assembly is
shown in Fig.-6(4).
Considering the vibration level for the motor and test zig,
Analog Device IC ADXL322 [13] has been selected for the
present work. This sensor measures acceleration with a full
scale range of 2g (typical). It can also measure both dynamic
(vibration) and static acceleration (gravity). The output voltages are proportional to the acceleration.
4.1 Sensor Packaging
In order to provide protection to the basic MEMS sensor with
signal conditioning electronics, it is necessary to encapsulate
them together in a way such that the complete encapsulated
module is easily mountable on the motor where the vibration
is to be monitored. To achieve this, a metal casing is designed
to house the MEMS sensor, pre-processing signal amplifier
connectors etc. Solid blocks of aluminum are used for making different parts of the housing. Broadly, the complete
housing is made of three parts and it’s detailed engineering
drawing is shown in Fig.-6.
Figure 7: Photograph of MEMS vibration sensor - design
and developed for this research work
The photographs of different components of vibration sensor
developed for this application are shown in Fig.-7.
Figure 8: Arrangement drawing of vibration transducers
Figure 6: Vibration sensor assembly detail (1) bottom part
(2) Main body with signal amplifier card (3) Top part with
circular connector, (a) MEMS PCB and (b) amplifier PCB
Different mounting arrangements for the sensor are possible.
In the present work, a threaded stud mounting arrangement,
as shown in Fig.-6(1), has been considered. This arrangement
gives the best contact between the sensor body and the vibrating body with a wide dynamic measurement range for
vibration. The basic MEMS sensor is also fitted on this part
Paper ID: J2013358
To reduce the stress on signal cable a provision is made for
proper anchoring of the cable, as shown in Fig.-8. The
mounting arrangement of the complete sensor on motor is
shown in Fig.-8.
4.2 Data acquisition system
A microcontroller based data acquisition board (MC-DAQ) is
developed and used here with two objectives:
• To use an independent data acquisition system with data
buffering feature to reduce the computational load on the
main central fault diagnostic system.
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
8 of 11
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
• To have a dedicated hardware that can be installed close
to the motor and can sustain the industrial harsh environment.
PIC-18F6520 series microcontroller [19] is used here for
developing the DAQ (data acquisition) board. The basic
schematic diagram is shown in Fig.-9.
Accelerometer 1X
‐
G1
+
Accelerometer 1Y
Accelerometer 2X
=
3
6
9
4
0
2
5
8
3
C
1
4
7
2
VDD1‐6
RD
0 1 2 3 4 5 6 7
D D D D D D D D
7 8 9 0
1 1
1 2
1 3
1 4
1
RD4‐7
C1+
C1‐
C2+
C2‐
RE
RF4 (AI5)
RF
C
RB0‐3
RF5 (AI6)
RC6
RC7
B
C
C
V
V+
V‐
T1OU
T2OU
R1OU R1IN
R2IN
D
T1IN
5
9
4
8
3
7
2
6
1
N
G
RG
A
1
SCL
SDA
WP
6
5
7
RC3
RC4
e
la
m
e
f
9
B
D
J1
MCLR
AT24C1024
The performance of MEMS vibration sensor is evaluated for
monitoring the vibration signal and associated frequency
components. The tests are conducted for the sensor fitted on
7.5KW induction motor under different operating conditions.
In vibration signal analysis, one of the main emphases is to
remove the noise and mitigate the unwanted vibration signal
(noise) originating from the sensor or other rotating machines
(operating in the vicinity) and the target machine. The effectiveness of the noise removal algorithm is checked with a
simulated signal. The noise signal, the original signal and the
corrupted signals are used to check the performance of the
noise removal algorithm, and are shown in Fig.-9 (a, b, c)
respectively.
MAX232
RB
RB4‐7
A1
4 5 6
RA
‐ RA0‐3
G5
+ RF4 (AI5)
D
S W
R R E
1 2 3
RD0‐2
‐
G4
+
SPARE
E
SS D
D E
V V V
(PIC18F6520)
‐
G3
+
Accelerometer 2Y
+
‐
X
..
Alpha‐numeric display
‐
G2
+
5. Results
VPP
VDD
VSS
RB7
RB6
RC
VSS1‐6
PGD
PGC
1
2
3
4
5
RJ12
Program.port
Figure 9: Schematic of microcontroller (PIC-18F6520)
based DAQ board developed for this work.
This MC-DAQ scans the data, stores locally and transmits it
over serial link to PC using simple ASCII protocol. The data
buffering can be done for a maximum of 30secs with a maximum scanning rate of 4kHz for 4 analog input channels. A
4x4 matrix membrane type key pad and a (2line) 16 character
LCD display are also provided to make the MC-DAQ board
more user friendly.
Figure 10: Test signal (a) white Gaussian noise signal,
(b) pure sinusoidal wave, (c) signal combining (a) and (b)
The effectiveness of auto-correlation function was examined
with different combinations of noise and signal ratio. The frequency spectrum of noisy signal has different peaks, other than
the fundamental frequency peak, as shown in Fig.-11 (a). After
applying the ACR filter, there is considerable suppression of
noise as illustrated in Fig.-11 (b).
4.3 Vibration transducer installation
The mounting of vibration transducers needs special attention
to faithfully reproduce the vibration of the frame (motor) at
the base of MEMS, by avoiding any type of rocking or bending of the transducer. Following steps are followed in this
work for creating proper mounting arrangement on motor
frame:
1. Flat surface creation on the motor frame above the bearing housing.
2. Milling and then drilling of the pilot hole of 3mm and
main drill of 6.9mm perpendicular to the surface. It is essential to prevent any damage to frame which is generally made of cast iron.
3. Drill depth shall be up to 8-10 mm
4. Tap creation in drill with M8 tap sets for tightening the
vibration transducers which have stud arrangement.
5. Cleaning of the surface and tightening the sensor into the
hole using a torque wrench, keeping in mind the MEMS
direction (as discussed above) and maintaining proper
contact between the base of the sensor and the mounting
surface.
Paper ID: J2013358
Figure 11: Illustrating the performance of noise removal
using auto-correlation function
The application of autocorrelation function for noise removal is
further substantiated by applying it on an actual rotating machine (in this case an induction motor). It can be observed in
Fig.- 12, that not only the low amplitude frequencies (multiple of
rotational frequency and floor noise) are suppressed but the actual signal level has also improved from 7.2X10−3 g to
11.7X10−3 g i.e −38dB to −42dB in amplitude.
For each fault, different fault combinations are built up by
changing the fault instant or arranging fault sequences, in order
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
9 of 11
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
to take into account the random nature of the fault instant. The
test results for different simulated faults with usual variability of
the signal spectrum are discussed here.
frequency components for the increase in loading are clearly
captured by the new sensor.
The typical axial and radial vibration spectra for healthy and
faulty motors are shown in Fig.-12. The x-axis and y-axis of
accelerometer signals are referred to here as axial (along the
motor shaft) and radial (radial to stator bore) vibration signal,
respectively. It is found that the vibration transducer is capable
of capturing these unique frequency peaks.
Figure 14: Illustration of radial vibration signal for different oscillatory load with (a) 0.02N-m, (b) 0.042N-m, (c)
0.084N-m
Figure 12: Demonstrating actual vibration signal with and
without ACR
More rigorous tests are carried out to check the ability to detect
fault frequencies under combined fault conditions. One such
result, for motor with (mixed) eccentricity and motor loaded
with an asymmetric load under different operating speeds are
shown in Fig.-15. Presence of 1X ( fr ) and ( 2 fr ) components
also indicates the presence of load imbalance.
A comparative analysis of axial and radial vibration frequency
components at different loads and at frequency ( f r = 12.2 Hz )
for healthy conditions are presented in Fig.-13. As expected, the
spectral information related to f r and 2 f r for radial signal in
the frequency spectrum is seen to be more prominent as compared to that for axial vibration.
Figure 15: Vibration spectra of a motor with eccentricity and load imbalance condition. (1) mixed eccentricity
with Load imbalance 0.0 ). (1) mixed eccentricity 42N-m
(2) Load imbalance 0.084N-m. Both the cases dynamic
eccentricity and static eccentricity are 21% and 19.7%
respectively.
The typical fault combination like misalignment and load imbalance, widely present in industrial environment (like metal rolling
mill) is depicted in Fig.-16.
Fig.-13: Typical power spectra of axial and radial vibration
signal of the machine rotating at 12.0Hz; under different load
conditions.
The experimental investigations on the excited vibration signals
of the machines sensed in axial and radial direction are further
carried out under different test conditions to ascertain the detectability of fault frequencies. The radial vibration pattern was
examined for different values of load torque oscillations) and
result is presented in Fig-14.
Unlike symmetric loading of healthy motor, where fr component
is the only prominent factor, load imbalance conditions depend
on 2 fr components as well. The increase in amplitude of these
Paper ID: J2013358
Figure 16: Typical spectral plot of motor vibration for combined fault (misalignment and load imbalance).
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
10 of 11
International Journal of Scientific Engineering and Research (IJSER)
www.ijser.in
ISSN (Online): 2347-3878
Volume 2 Issue 9, September 2014
The results demonstrate that the 1X and 2X components in
axial and radial vibration signal are clearly detectable. The
combination of the 1X and 2 X components in axial (Fig.16-a
and b) and radial vibration (Fig.16-c and d) indicates the
presence of misalignment and load imbalance, as expected.
6. Conclusion
This paper has dealt with the design and the developmental aspects related to a low cost vibration sensor. To improve the characteristic signal detection capability in the acceleration signal, a
new noise cancellation algorithm using auto-correlation function
and adoptive threshold based filters is developed in this paper.
The potential of the proposed signal processing technique has
been assessed under different operating and fault conditions, in
order to extract the fault feature frequencies of the weak fault
signals in the presence of strong noise. These methods have been
proved to be very effective for filtering the periodic, white
Gaussian and random noise in real-time acceleration signals.
tion sensing,” Sensors and Actuators A, vol. 113, no. 1,
pp. 20–38, 2004.
[9] J.P Sebastia, J.A. Lluch, J.R. Vizcaino and J.S. Bellon,
“Vibration Detector Based on GMR Sensors”, IEEE
Transactions on Instrumentation and Measurement, vol.
58, no.3, 2009, pp 707-712.
[10] A. Albarbar, S. Mekid, A. Starr, and R. Pietruszkiewicz,
“Suitability of MEMS accelerometers for condition monitoring: An experimental study,” Sensors (Basel), PMCID:
PMC3672998, vol. 8, no. 2, pp. 784–799, 2008.
[11] H.Xie, G. Fedder, “CMOS z-axis capacitive accelerometer with comb-finger sensing”. In Proc. IEEE Micro Electro Mechanical Systems (MEMS), 2000; pp. 496-501.
[12] V. Biefeld, A. Buhrdorf and J. Binder, “Laterally driven accelerometer fabricated in single crystalline silicon”, Sensor
Actuators, vol. 82(1), 149-154, 2000.
[13] J. Sinha, “On Standardisation of Accelerometers”. Journal of
Sound and Vibration 2005, 286, 417-427.
References
[14] Analog devices, http://www.dimensionengineering.com/
datasheets/ADXL322.pdf.
[1] M. E. Elnady, J. K Sinha and S. O. Oyadiji, “Condition
monitoring of rotating machines using on-shaft vibration
measurement. In: Proceedings of the IMechE, 10th international conference on vibrations in rotating machinery, London, UK, 11–13 September 2012.
[15] S. E. Lyshevski, “MEMS and NEMS: systems, devices and
structures” CRC Press LLC, USA, 2002.
[2] A. Muszynska, “Vibrational Diagnostics of Rotating
Machinery Malfunctions”, International Journal of Rotating Machinery, vol1 Issue 3-4, pp 237-266, 1995
[3] J. K Sinha and K. Elbhbah “A future possibility of vibration based condition monitoring of rotating machines”,
Mechanical Systems and Signal Processing, vol 34: pp
231–240, 2013.
[4] F. Jiang, W. Li, Z. Wang, and Z. Zhu, “Fault Severity Estimation of Rotating Machinery Based on Residual Signals,”
Advances in Mechanical Engineering, pp. 1-8, 2012.
[5] N. Tandon and A. Choudhury, “A review of vibration
and acoustic measurement methods for the detection of
defects in rolling element bearings,” Tribology International, vol. 32, pp. 469–480, 1999.
[16] H. Luo, G. Fedder, and L. Carley, “A 1 mg lateral CMOSMEMS accelerometer,” Proceedings IEEE Thirteenth Annual International Conference on Micro Electro Mechanical
Systems, 2000.
[17] Yazdi, N.; Ayazi, F.; Najafi, K. “Micromachined inertial
sensors”, Proc. IEEE, Vol.86, no. 8, pp. 1640-1659, 1998.
[18] F. Mohn-Yasin, C. E. Korman, and D. J. Nagel, “Measurement of noise characteristics of MEMS accelerometers,” Solid-State Electronics, vol. 47, pp. 357–360,
2003.
[19] Microchip, PIC18F6520/8520/6620/-- Data Sheet”,
http://www.datasheetarchive.com/PIC18F6520-8520datasheet.html
[6] W. R. Finley, M. M. Hodowanec, and W. G. Holter, “An
analytical approach to solving motor vibration problems,” in IEEE-Ind. Appl. Society, 46th Annual Petroleum and Chemical Industry Conf., Knoxville, TN, 13-15,
pp. 217–232, Sept. 1999.
[7] G. Perrone and A. Vallan, “A low-cost optical sensor for
noncontact vibration measurements,” IEEE Transactions
on Instrumentation and Measurement, vol. 58, no. 5, pp.
1650–1656, 2009.
[8] T. K. Gangopadhyay, “Prospects for Fiber Bragg gratings and Fabry-Perot interferometers in fiber-optic vibra-
Paper ID: J2013358
LICENSED UNDER CREATIVE COMMONS ATTRIBUTION CC BY
11 of 11
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement