IRJET-Condition Monitoring based Control using Piezo Sensor for Rotating Electrical Motors

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1297
CONDITION MONITORING BASED CONTROL USING PIEZO SENSOR FOR
ROTATING ELECTRICAL MOTORS
Sanjeev Kumar H. Yadav1, Prof. E. Vijay kumar2, Prof. Vivek kumar yadav3, Prof. Abijeet patil4
Student, Dept. of Electrical Engineering, R.K.D.F. Institute of Science & Technology, Bhopal, Madhya Pradesh, India
Associate Prof. /H.O.D., Dept. of Electrical Engineering, R.K.D.F. institute of Science & Technology, Bhopal, Madhya Pradesh,
India
3,4Asst. Prof., Dept. of Electrical Engineering, R.K.D.F. institute of Science & Technology, Bhopal, Madhya Pradesh, India
--------------------------------------------------------------------***-------------------------------------------------------------------
Abstract: A protection system for rotating electrical motors
through condition monitoring to identify the problem the
problematic source in the motor and to protect due to
excessive vibration without human observation.
Maintenance of electrical motors is important the condition
monitoring of motor is gaining importance in industry to
reduce the downtime time costs and increase motor
reliability. In the work a simulation model has been
developed using piezoelectric sensor for condition
monitoring to detect the problem .If any problem is occurred
in any location in the motor then vibration of the whole
system increases. It extracts the maximum value of vibration
signals coming from different bearing positions of the motor.
If the magnitude of vibration is in “unacceptable” range. If
the value of vibration is in “unsatisfactory” range then an
alert on the computer screen .The “unsatisfactory” and
“unacceptable” condition it displays of defective part. This
system not only protects the unscheduled shut down of
motors but also increases the lifetime of motor components
in the present paper, MATLAB/SIMULINK software is used to
diagnosis the different fault of induction motor. Fast Fourier
Transform (FFT) is used for diagnosis of the different fault in
induction motors. The results and observations obtained are
discussed and then final conclusions are made.
Keywords: Condition Monitoring, Vibration Analysis,
motor current signature analysis, Fast Fourier
Transform, MATLAB/SIMULINK.
I. INTRODUCTION
Induction motors finds application in almost every kind of
industries all around the world. Although, Induction
motors are extremely consistent, they are vulnerable to
many types of faults. There are many methods available for
detection of faults in Induction Motor but most of the
methods require expensive sensor or specialized tools
whereas current monitoring out of all does not require
additional sensors. The Motor Current Signature Analysis
uses the current spectrum of motor for locating
characteristic fault frequencies.
There are countless faults in a simple motor, but these
faults are difficult to detect so that any person has to go to
the motor when the motor is spoiled and find that fault. In
this project we have explored the types of motor and the
motor fault and the causes of fault and the technique to
improve it. In this project we have studied about motor
and the faults in motor. We know that there are a lot of
fault inside the motor. And when the motor is running then
it is very difficult to detect these faults.
And until we detect the fault, the motor has gone dead.
Therefore, it is very difficult to detect the fault in the motor
at the running time. We have only used this project to
study. So inside this project we have used two types of
work. Fast Fourier Transform, MCSA (motor current
signature analysis), Condition Monitoring, Vibration
Analysis, MATLAB/SIMULINK.
In this project paper we will see how we have studied a
motor. And at the same time, using some techniques, we
can increase the efficiency of the motor and its capacity
and its life. Through this project, we want to show how we
can check a motor during the running condition. We will
see How Can We Monitor on The Real Time Basis to the
Motor. And without stopping the motor, we can know its
condition. During each second we will be able to find every
single condition of the motor. Under what conditions the
motor is now working and what kind of improvement it
can be made. All this we can check by a simulation model.
Through this simulation, we will study different waveform.
By these waveform, we can study the motor accurately And
detect changes in the motor. And through this, we can save
prematurely accidents in the motor. Motor can be saved
from premature deterioration and its life can be increased.
II. VIBRATION DIAGNOSIS
Vibration control and vibration diagnostics are Different
practical problems. In vibration diagnostics, the oscillation
force that is applied to the defective zone defines the fault
and the force is linearly related with the oscillation
acceleration. For diagnostics, often both the vibration-
acceleration and the vibration-velocity are measured in
restricted low frequency ranges.
Most vibration measurements usually use sensors of
vibration-acceleration that work based on the piezoelectric
effect. For this type of sensors the output electric charge is
proportional to the force applied to the sensor. The
vibration signal is converted in electric signals. It is
necessary to analyses this signal without losing the

diagnostic information. There are very strict requirements
for the analyzing instruments. The operations that the
vibration analyzing instruments must perform are the
following:
 Measurement of overall vibration level in a
standard frequency range and using the units
required by these standards.
 Spectral analysis of the vibration, by using FFT.
 Analysis of the oscillation power of separate
vibration components extracted preliminary from
the vibration signal. The analysis of the spectrum
of random high frequency vibration signal is
usually used.[9].
Fast Fourier Transform
Many different techniques can be found for the fault
diagnostics of an induction motor. All of them have their
own benefits and drawbacks. In this case, fast Fourier
transform was chosen to be a sufficient diagnostic method
for induction motor fault detection.
Due to the advancement of digital signal processing
techniques the role of frequency in the signal become very
important part of the detection of the fault in the induction
machine. The fast Fourier transform is the advance version
of the discrete Fourier transform, the data computing time
is less in the FFT than discrete Fourier transforms [3]. The
FFT algorithm is widely used for the detection of rotor
broken bar fault, motor fault etc. It is possible to find out
power spectral density of the motor stator current [4]. The
stator current shows the side bands around the
fundamental frequency. The fundamental frequency of the
motor is 50Hz. If any fault is occurred then amplitude of
the side lobes are increased that is clear indication of fault.
III OUTLINE OF THESIS REPORT
The studies of induction motor behavior during abnormal
condition due to presence of faults and possibility of
diagnose this abnormal condition have been a challenging
topic for the many electrical machine researchers. There
are many conditions monitoring method including
vibration monitoring, thermal monitoring, chemical
monitoring acoustic emission, but all these monitoring
method required expensive sensor [2] or specialized tools
whereas current monitoring out of all does not required
additional sensors. The first methods utilized to detect
motor failures, such as chromatographic analysis, noise
analysis, temperature analysis, and vibration analysis have
been slowly changing to new on-line monitoring
techniques for electrical equipment. The various advanced
signal processing techniques such as Fast Fourier
Transform, Short Time Fourier Transform, Gabor
Transform, and Wavelet Transform are used to diagnose
the faults of induction motor [5]. A suitability of the signal
for different type of faults is also discussed in detail. FFT is
easy to implement but the drawback of this technique is
that it is not suitable for analyzing transient signals.
Although Short-Time Fourier Transform (STFT) can be
used for analyzing transient signals using a time-frequency
representation, but it can only analyze the signal with a
fixed sized window for all frequencies, which leads to poor
frequency resolution.
It is important to detect the fault in induction motors in an
early stage itself due to the cut-throat competition and
stringent reliability standards. As far as the stationary
signals are concerned, conventional signal processing
techniques such as FFT analysis, cosine transform etc.
were used earlier to analysis the fault condition and are
proved to be working perfectly. Among the total induction
motor faults, around 30-40 % are related to the stator
winding insulation and core. Among the possible causes,
thermal stresses are the main reason for the degradation
of the stator winding insulation. Generally, stator winding
solution thermal stresses are categorized into two types:
aging and overloading. Electrical stresses, mainly related
to the machine terminal voltages, also cause insulation
degradation.
Vibration problems can occur at any time in the
installation or operation of a motor. When they occur it is
normally critical that one reacts quickly to solve the
problem. If not solved quickly, one could either expect long
term damage to the motor or immediate failure, which
would result in immediate loss of production. The loss of
production is oftentimes the most critical concern. To solve
a vibration problem one must differentiate between cause
and effect. For this to happen, one must first understand
the root cause of the vibration. In other words: where does
the force come from? Is the vibratory force the cause of the
high levels of vibration or is there a resonance that
amplifies the vibratory response. Perhaps the support
structure is just not stiff enough to minimize the
displacement. In this paper the various sources of
electrical and mechanical forces will be explained.
Additionally, how the motor reacts or transmits this force
and how this force can be amplified or minimized will be
explained as well.
IV. BACKGROUND OF PROJECT
Induction motor have been widely use in high-
performance electrical drive and industries. There are
many differences Induction Motor in the market and all
with it good and bad attributes. Such bad attribute is the
lag of efficiency. In order to overcome this problem fault
detection is introduce to the system. There are also many
types of fault detector used in the industry. The
productivity of the industrial systems based on these
drives is very critical. Thus reliability of this operation is of
paramount importance. The reliability of this operation is
frequently impacted by faults which affect this operation.
Thus the study of performance of these machines under
fault conditions gives better idea of their reliable operation
perhaps even indicating better choices while designing

them. Thus the primary focus of this work is to investigate
by modeling, simulating and related case studies.
V. OBJECTIVE
The objectives of this project are:
i. To fulfill the requirement for the Engineering
Project.
ii. To explorer and apply the knowledge gain in
lectures in to practical applications.
iii. To fault diagnosis the fault of Induction motor
with motor current signature analysis and
comparing with Fast Fourier Transform (FFT)
using MATLAB simulation software.
iv. To analyze the behavior of the induction
motor under faulty conditions.
v. To design the motor current signature
analysis and Fast Fourier Transform (FFT)
and tune it using simulation.
vi. To compare and analyze the result between
motor current signature analysis and Fast
Fourier Transform (FFT) simulation result
using Induction motor in MATLAB.
vii. This research work is to investigate how the
presence of common faults, such as rotor bar
fault, short winding fault, air gap eccentricity,
bearing fault, load fault, effect on different
fault frequencies under different load
conditions .
viii. In this research work, condition monitoring
and fault detection of induction motors is
based on the signal processing techniques.
The signal processing techniques have
advantages that these are not computationally
expensive and these are simple to implement.
Therefore, fault detection based on the signal
processing techniques is suitable for an
automated on-line condition monitoring
system. Signal processing techniques usually
analyze and compare the magnitude of the
fault frequency components, where the
magnitude tends to increase as the severity of
the fault increase. Therefore, the third aim of
this thesis is to utilize the various signal
processing techniques for detection of
common faults of induction motor.
Signal processing techniques have their limitations. For
example, some faults could be not diagnosed using Fast
Fourier Transform, if the loading condition is too low or
the fault is not too severe [6]. Therefore, the final aim of
this thesis is to investigate new features using different
techniques such as Fast Fourier Transform (FFT), to find
better features for detecting common faults under
different loading conditions.
VI. NEED FOR CONDITION MONITORING
Rotating electrical machines are the essential components
in industry. The unscheduled shut down caused by a
failure of machines can cause enormous costs. The most
common types of faults in rotating electrical machines
have been categorized according to the different
components of machines [11,12] -(i) Stator fault (37%), (ii)
Rotor fault(10%), (iii) Bearing faults (41%), (iii) Other
fault (12%).
Condition monitoring is defined as the continuous
evaluation of the health of the plant and equipment
throughout its service life. It is important to be able to
detect faults while they are still developing. This is called
incipient failure detection [7]. The incipient detection of
motor failures also provides a safe operating environment.
It is becoming increasingly important to use
comprehensive condition monitoring schemes for
continuous assessment of the electrical condition of
electrical machines. By using the condition monitoring, it is
possible to provide adequate warning of imminent failure.
In addition, it is also possible to schedule future preventive
maintenance and repair work. This can result in minimum
down time and optimum maintenance schedules [8].
Condition monitoring and fault diagnosis scheme allows
the machine operator to have the necessary spare parts
before the machine is stripped down, thereby reducing
outage times. Therefore, effective condition monitoring of
electric machines is critical in improving the reliability,
safety, and productivity.
EXISTING CONDITION MONITORING TECHNIQUES
This research is focused on the condition monitoring and
fault diagnosis of electric machines. Fault diagnosis is a
determination of a specific fault that has occurred in
system. Condition monitoring has great significance in the
business environment due to following reasons:
 To reduce the cost of maintenance
 To predict the equipment failure
 To improve equipment and component reliability
 To optimize the equipment performance
 To improve the accuracy in failure prediction
The condition monitoring of electrical and mechanical
devices has been in practice for quite some time now.
Several methods have evolved over time but the most
prominent techniques are thermal monitoring, vibration

monitoring, and electrical monitoring, noise monitoring,
torque monitoring and flux monitoring [5].
The most commonly used condition monitoring system is
the vibration-based condition monitoring. The vibration
monitoring technique is based on the principle that in
running condition all systems produce vibration. When a
machine operates properly, vibration range is small;
however, when a fault develops and some of the dynamic
processes in the machine change, the vibration also
changes. Vibration based condition monitoring is the most
reliable method to assess the overall health of a rotating
motor drive system. Vibration based condition monitoring
system requires storing of a large amount of data. Multiple
sensors are mounted often to measure the vibration on
different parts of the machine. In abnormal condition the
machine vibration will be very high.
VII. SIGNAL PROCESSING TECHNIQUES FOR FAULT
DETECTION IN INDUCTION MOTOR
The first step for condition monitoring and fault diagnosis
is to develop an analysis technique that can be used to
diagnose the observed current signal to get useful
information. There are several signal processing
techniques which are very useful for fault diagnosis
purpose. These are classified below [5, 14, and 15]:
1. Frequency domain: Fast Fourier Transform (FFT)
2. Time-Frequency techniques:
a. Short Time Fourier Transform (STFT)
b. Gabor Transform (GT)
3. Wavelet Transform (WT)
4. Time series methods
5. Fuzzy logic
6. Artificial Intelligent
7. PI controller
VIII. FAULT DETECTION FOR THE MOTOR CURRENT
SIGNATURE ANALYSIS (MCSA) AND FAST FOURIER
TRANSFORM (FFT):
The techniques of fault diagnosis consist of using fast
Fourier transform (FFT) or MCSA or extraction
component, feature cluster component, and the fault
decision component. In order to diagnose the fault in stator
short circuit, Motor Current Signature Analysis or (MCSA)
is utilized. Techniques of signal processing such as the FFT
are formed on the basis of load, fundamental frequency of
constant stator, assumption on sufficient load and the
speed of motor.
Figure 1. Simulink model of an induction motor
In this simulation model, we have analyzed the motor.
First, the normal condition of the motor has been kept. In
which initial value of different parts of the motor has been
entered. After this the Normal condition of the motor was
prepared. After considering the normal condition of the
motor, separate value of the motor is different from the
initial value of the motor has been entered. In the
simulation model, comparison of different parts of the
motor in normal and abnormal condition is considered.
After analyzing, the fault in the motor is detected.
IX. RESULT DISCUSSION AND CONCLUSION
The below figure shows the voltage and current waveform
for the Simulink model of figure 1 where the output will be
normal till the fault occurs. At the time the fault occurs, the
input trips off and the motor stops its operation.
Figure 2. Simulation output of IM during normal operation
The above figure 1 shows Simulink model of an Induction
Motor. The induction motor was simulated during starting

from rest with rated voltage applied and no mechanical
load. health of induction motor, Stator input voltage, speed
and torque, symmetrical components of stator current,
symmetrical components of stator induced voltage,
symmetrical components of stator input voltage are
shown. The above simulation runs for 2.0 seconds. The
motor is started from rest with rated voltage and no load.
From the output of the FFT it is seen the health of the
motor remains good after the transient time. Figure 2
shows the output of IM and FFT output.
The above figure 2 shows the output waveform for IM
which shows the normal flow of motor current till the fault
occurs, when the fault occurs the motor operation stops.
The above figure shows the voltage and current waveform
for the Simulink model of figure 2 where the output will be
normal till the fault occurs. At the time the fault occurs, the
input trips off and the motor stops its operation.
A broken bar causes several effects in induction motors. A
well know effect of a broken bar is the appearance of the
so-called sideband components. These sidebands are found
in the power spectrum of the stator current on the left and
right sides of the fundamental frequency component.
Other electric effects of broken bars are used for motor
fault classification purposes including speed oscillations,
torque ripples, instantaneous stator power oscillations,
and stator current envelopes. Here the fault monitoring
method is based on torque ripples for broken bar
detection, while the fault diagnostic method is based on the
three-phase stator current envelope for classification of
broken rotor bars and inter-turn short circuits.
Figure 3. Simulation output of IM during faulty condition.
The above figure shows the speed and torque waveform
which is abnormal condition after few oscillations and
when a fault occurs.
The above figure 3 shows the voltage and current
waveform for the Simulink model of IM where the output
of motor is abnormal. Figure 3 shows the faulty condition
of the motor At the time the fault occurs, the input trips off
and the motor stops its operation.
In the hardware model, using the Piezo sensor, with the
help of vibration, sensor has detected changes in the
motor. The fault has been detected by placing the sensor
on the body of the motor. After comparing vibration of
motor, the normal and abnormal condition is detected.
Figure 4. 50 rpm geared motor in normal condition
figure 4 shows the output signal of 50 RPM geared motor
in normal condition. After detecting the vibration signal
from motor with the help of piezo sensor, output of the
motor vibration is as shown in the figure 4. This output
signal shows the normal operation of motor. When the
motor works on the normal condition, then the vibration
inside the motor is much less. In the normal operation of
motor vibration spikes is low as shown in the figure 4.
Figure 5. 50 rpm geared motor in damage condition
Figure 5 shows the output signal of 50 RPM geared motor
under faulty condition. After detecting the vibration signal
from motor with the help of piezo sensor, output of the
motor vibration is as shown in the figure 5. This output
signal shows the faulty condition of motor. In the faulty
condition of motor vibration spikes is high as compare to
normal condition as shown in the figure 5. It shows

warning alert message on the screen when piezo sensor
detects the fault. When the motor works on the abnormal
condition compared to the normal condition, then
vibration increases inside the motor. If the fault is not
corrected, then gradually this vibration in the motor
increases and the motor will be damaged.
X. CONCLUTION
The condition monitoring of induction motors have been a
challenging task for the engineers and researchers mainly
in industries. In the simulation model, comparison of
different parts of the motor in normal and abnormal
condition is considered. In the hardware model, using the
Piezo sensor, with the help of vibration, sensor has
detected changes in the motor. In the above output we
have tested on different type geared motor which
generates different output through which we can analysis
the amount of vibration generated by the motors.
In this work, the basic concepts about induction motors
and ac drives, as well as the various types of induction
motor faults has been presented. Moreover, the physical
phenomenon associated with each of these faults was
described, and the features of the induction motor
performance resulting from these faults was presented
using the Simulink model for each these faults.
The aim of this work is to advance the field of condition
monitoring and fault diagnosis in induction motor
operating in variety of operating conditions. With the help
of condition monitoring we can easily avoid the critical
emergency shutdown as well as reduce the maintenance
costs of motor other faults. Electrical Fault such as
unbalanced voltage supply, single phasing. Condition
monitoring, signal processing and data analysis are the key
parts of the Motor fault detection scheme.
XI. ACKNOWLEDGMENT
I would like to express my sincere gratitude to Prof. E.
Vijay kumar for his constant guidance and he appreciated
my effort and his contribution is extremely valuable to me.
I would like to express my sincere gratitude to Prof. Vivek
kumar Yadav, who motivated me and his valuable inputs. I
would like to thank Prof. Abijeet Patil that he gave me
confidence.
Also I would like to thank all those who have helped me
and supported me throughout my dissertation work.
XII. REFERENCE
1. T. A. Kawady, A. A. Afify, A. M. Osheiba, A. I. Taalab,
“Modeling and Experimental Investigation of
Stator Winding Faults in Induction Motors “ ,
Electric Power Components and Systems, Volume
33, October 2008, pp.599-611.
2. Khadim Moin Siddiqui, Kuldeep Sahay, v.k.
Giri“Rotor broken bar fault detection in Induction
Motor Using Transformative Techniques”,
Institute of Engineering & Technology, Lucknow,
INDIA.
3. Toomas Vaimann, Ants Kallaste, “Detection of
broken rotor bars in three-phase squirrel-cage
induction motor using fast Fourier transform”,
10th International Symposium,Topical Problems in
the Field of Electrical and Po wer
Engineering,Pärnu, Estonia, January 10-15, 2011.
4. Benbouzid M., Kliman G., “What Stator Current
Processing-Based Technique to Use for Induction
Motor Rotor Faults Diagnosis”, IEEE Transactions
on Energy Conversion, vol. 18, no. 2, June 2003,
pp. 238–244.
5. Siddharth ukey, “Comparative Analysis between
Proportional Integral Controller and Wavelet
Controller for the Fault Detection in Induction
Motor”, International Journal of Thesis Projects
and Dissertations (IJTPD) Vol. 1, Issue 1, PP: (17-
44), Month: October-December 2013.
6. M. S. Ballal , H. M. Suryawanshi , Mahesh K. Mishra
, “Stator Winding Inter-turn Insulation Fault
Detection in Induction Motors by Symmetrical
Components Method ”, Electric Power
Components and Systems, 36: July 2008, pp.741–
753.
7. T. A. Kawady, A. A. Afify, A. M. Osheiba, A. I. Taalab,
“Modeling and Experimental Investigation of
Stator Winding Faults in Induction Motors “ ,
Electric Power Components and Systems, Volume
33, October 2008, pp.599-611.
8. A.H. Bonnett, G.C.Soukup, “Cause and Analysis of
Stator and Rotor Failures in Three- Phase Squirrel-
Cage Induction Motors,” IEEE Transactions on
Industry Applications, vol. 28 No. 4, July/August
1992, pp.921-937.
9. Hamid Toliyat, Subhasis Nandi, Condition
monitoring and fault diagnosis of electrical
machines, IEEE 1999.
10. M. Iorgulescu, R. Beloiu, M. O. Popescu- Induction
motors monitoring based on artificial neural
network, 10th International Conference on
Optimisation of Electrical and Electronic
Equipment, Brasov, 18 – 19 May, 2006, vol. III,
ISBN 973-635-705-8, 978-973-635-702-2, pag.
157 – 162.
11. Arunava Kabiraj Thakur, T.M. Joardar, Pranab K
Dan, “MODELLING A CONDITION MONITORING

BASED CONTROL USING MATLAB/SIMULINK FOR
ROTATING ELECTRICAL MACHINES”,
International Journal of Recent advances in
Mechanical Engineering (IJMECH) Vol.2, No.1,
February 2013.
12. L. Navarro, M. Delgado, J. Urresty, J. Cusidó, L.
Romeral ,(2010) “Condition monitoring system for
characterization of electric motor ball bearings
with distributed fault using fuzzy inference tools”,
Instrumentation and Measurement Technology
Conference (I2MTC), IEEE, Page(s): 1159-1163.
13. Jordi Cusido, Luis Romeral, J A Ortega, J A Rosero
and A G Espinosa, “Fault Detection in Induction
Machines Using Power Spectral Density in Wavelet
Decomposition”, IEEE Transactions on Industrial
Electronics,Vol.55, N0.2, February 2008, pp 633-
643.
14. Sergio M. A. Cruz and A. J. Marques Cardoso,
“Multiple Reference Frames Theory: A New
Method for the Diagnosis of Stator Faults in Three-
Phase Induction Motors”, IEEE Transactions on
Energy conversions, Vol. 20, No. 3, September
2005.
15. Xu Bo-qiang, LI He-mhg, SUN Li-hg, “Detection of
Stator Winding Inter-turn Short Circuit Fault in
Induction Motors”, 2004 International Conference
on Power System Technology - POWERCON 2004
Singapore, 21-24 November 2004, pp.1005-1009.
16. M. da Silva, R. J. Povinelli, and N. A. O. Demerdash,
"Induction Machine Broken Bar and Stator Short-
Circuit Fault Diagnostics Based on Three Phase
Stator Current Envelopes," IEEE Transactions on
Industrial Electronics, 2006.
17. N. M. Elkasabgy, A. R. Eastham, and G. E. Dawson,
"Detection of broken bars in the cage rotor on an
induction machine," IEEE Transactions on
Industry Applications, vol. 28, pp. 165-171, 1992.
18. S. Hsu, H. H. Woodson, and W. F. Weldon, "Possible
errors in measurement of air-gap torque
pulsations of induction motors," IEEE
Transactions on Energy Conversion, vol. 7, pp.
202-208, 1992.
19. M Haji, S. Ahmed, and H. A. Toliyat, "Induction
machines performance evaluator 'torque speed
estimation and rotor fault diagnostic'," in
Proceedings of the Seventeenth Annual IEEE
Applied Power Electronics Conference, vol. 2, pp.
764-769, 2002.
20. V. V. Thomas, K. Vasudevan, and V. J. Kumar,
"Implementation of online air-gap torque monitor
for detection of squirrel cage rotor faults using
TMS320C31,"in Proceedings of the International
Conference on Power Electronics, Machines and
Drives, pp. 128-132, 2002.
21. I.C. Report, "Report of large motor reliability
survey of industrial and commercial installation,
Part I and Part II," IEEE Transactions on Industry
Applications, vol. 21, pp. 853-872, 1985.
22. S. Nandi, H. A. Toliyat, and X. Li, "Condition
Monitoring and Fault Diagnosis of Electrical
Motors - A Review," IEEE Transactions on Energy
Conversion, vol.20, pp. 719-729, 2005.
23. www.google .co.in

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