Fault Detection in Three Phase Induction Motor
using Artificial Intelligence: A review
P.D.Duhat , C.A.Kshirsagar
Dept. of Electronics & Power, Dept. of Electronics & Telecomm,
Amaravati University
B.N.C.O.E,Pusad(India)
pdduhat@rediffmail.com
chanchal.kshirsagar@gmail.com
Abstract— This paper describes a overview of induction motors
signature analysis as medium for fault detection, as well it
describes a generalized model of the three phase induction motor
using matlab simulink. This paper aims at developing mat lab
based machine model with neuro fuzzy techniques for detecting
the faults of induction motor.
Keywords- neurofuzzy technique, motor signature analysis,
simulink model, neural network, induction motor
I. INTRODUCTION
Induction motor are used in many fields of industrial
production system, due to their advantages like quick start,
less maintenance etc. Long term working failure is very
crucial for production system. Like other types of electrical
machines these motors are exposed to a wide variety of
environmental conditions electrical and mechanical errors.
Because of these motor defects will bring about labour and
maintenance cost. Motor operating life is reduce if these faults
left undetected. In addition, it causes delay of production
process and financial losses , due to stopping of motor.
Therefore, it is always desirable to provide
prevention of motor failure rather than protection. This is
achieved by continuous condition monitoring. It avoids
unexpected failure of a critical system. Hence, there is a
considerable demand for electrical machines and drive system
with fault diagnostics and prediction features. Before studying
fault diagnostic methods, it is necessary to know the most
common faults found in induction motor . The faults occur in
a motor depends upon construction and working conditions, it
has two main parts stator and rotor, thus faults are classified
according to their location that is in stator part or in rotor
parts. These faults are classified as follows:
Stator Faults: 1) Abnormal connection of the stator winding
2) Turn to ground faults
3) Opening or shorting of the stator winding
4) Stator core related faults
Rotor Faults: 1) Rotor winding related fault
2) Bearing and gear box faults
3) Eccentricity related faults
4) Broken rotor bar fault
While detecting the above mentioned faults in an induction
motor many parameters are taken into consideration such as
temperature, vibration, noise, supply voltage, current and load
etc. The main causes of stator core failure are as follows:
1) Core end region heating resulting from axial flux in
the end winding region.
2) Core melting caused by ground faults currents.
3) Manufacturing defects in laminations
4) Inter laminar insulation failure.
5) Stator rotor rubs during assembly and operation.
Rotor faults: The most common rotor faults
in an
induction motor may be classified as:
1) Rotor winding related faults
2) Broken rotor bar faults
3) Bearing and gearbox faults
4) Eccentricity related
There are some parameters of motor which are affect when
faults occur in a rotor such as short circuit turns in rotor
windings cause operational problems that is a vibration levels.
Bearing fault may account for 42 – 50% of all motor failure.
The bearing fault may lead to excessive audible noise, uneven
running, reduced working accuracy and increased vibration.
Eccentricity is a condition of the asymmetric air gap
between stator and rotor , eccentricity develops high
unbalanced radial forces ,vibrations and noise in the motor.
Thus each occurred faults will produce some uneven
parameters of the motor such as vibration, noise, unbalanced
voltage etc. Thus by condition monitoring we can identify the
actual fault occurred.
II LITERATURE REVIEW
There are fault monitoring techniques such as Invasive
and Non-invasive methods .These can be explained as model
based technique, signal processing technique, AI based
techniques etc.
A. Model Based Technique :
This technique is categorized into invasive method by using
the actuators and sensors the task consist of the different
measurable signals. These measurable signal consist of some
dependencies. These signal dependencies are expressed by
mathematical model. Issermann [12] has presented a unified
model based fault detection and prediction (FDP) techniques.
Based on the measured signal the detection model generates
residuals, parameter estimation or state estimates which are
called features. By comparing these measured features with
the normal features, the changed features are detected leading
to analytical symptoms. This technique is developed for non –
linear multiple input multiple output discrete time system. The
stability of the fault detection and prediction scheme enhances
the detection and time to failure accuracy.
The main disadvantage of these methods is it requires
accuracy while building mathematical model so more time
spent for building the model, possibly more complex design
procedure.
B. Signal Processing Technique:
This is also known as non –invasive method. The faults in
induction motor are detected by using stator current analysis.
The above proposed system was able to diagnosis of induction
motor faults such as breakage of rotor bar, air gap
eccentricity , bearing faults, fault in stator winding etc. this
method also be applied on line[2],[1].
As in [3] some of the main stator current
signature depending upon the frequency analysis is:
Classical (FFT) fast Fourier transform techniques are applied
to the measured sensor signals in order to generate features or
parameters. As in [4] instantaneous power FFT in which
instantaneous power is used as a medium for motor signature
analysis. However the power spectra are still noisy.
Bispectrum is term of the two dimensional Fourier transform
of the third order sequence of process, this preserves both
magnitude and phase information. The technique should be
particularly applied to detect electrical based faults.
High resolution spectral analysis based on Eigen analysis of
the autocorrelation matrix in the digital signal, that is keep
only the principle spectral components of signal and to
decrease noise. With the above analysis wavelet and short
time Fourier techniques (STFT) are used which analyse small
sections of the signal at a time. Park’s vector analysis in [2]
based on the locus of the instantaneous spatial vector sum of
three stator currents that allows the detection of faulty
condition by monitoring the deviation of the acquired pattern.
Results obtained by the parks vector approach are more
discriminative than those obtained by traditional FFT based
MCSA.
The above conventional methods are expensive
to realize are unsuitable for online implementation, require the
knowledge of an expert or utilize a rigorous mathematical
model. To overcome this problem, soft computing techniques
such as artificial intelligence, artificial neural network (ANN),
fuzzy system etc. have been employed to build model based
fault detection technique.
C. Soft Computing Techniques
These are the non-invasive measurement technique
which are inexpensive and as in [6] ANN is proposed for fault
detection and identification, it can be used as computational
model of the brain. As in [7] has suggested a neural network
approach for the detection and location automatically of an
inter turn short circuit fault in the stator windings of an
induction motor. a multilayer feed forward neural network
along with back propagation algorithm are used to train the
neurons.
Neural network is the computational model which
cannot provide the heuristic interpretation of the solution. The
neural network with fuzzy logic principle enables the
neurofuzzy system to provide more accurate fault detection
and knowledge behind the fault detection. In [11] induction
motor have been applied an adaptive neural fuzzy inference
system for detection of inter turn insulation and bearing wear
faults. it proved here that ANFIS technique provide more
accurate results. As in [10] fuzzy logic methodology used for
detection of stator winding fault in induction motor. The fuzzy
logic method able to detect the motor condition with high
accuracy for both noise and without noise. The stator current
three phase rms values have been used as the input to the
fuzzy logic system. The drawback of this method is a current
unbalanced originating from the supply source may be
identified as a faulty condition of the motor.
In [7] generalized feed forward neural network is
used for fault detection the number of input processing
elements are equal to that of number of statistical parameters.
Minimum thirteen parameters are calculated with RMS value
of zero mean signals. These parameters are classifying under
four conditions to developed generalized feed forward neural
network. In [8] support vector machine are learning machines
that are able to avoid the curse of dimensionality in both
computation and generalization. Various fault of induction
motor are detected using SVM to classify different faults, the
statistical parameters are calculated from current data. These
statistical parameters are RMS value of zero mean signal,
maximum and minimum values that is skewness coefficient
and kurtosis coefficient. Here SVM is used for non linear
mapping to generate classification feature from the original
data. The SVM classification task consist of selection of
kernel function. It consist of RBF, Linear, and MLP kernals
which are tested using matlab.
Thus total data which includes thirteen input features
is divided into three groups. These three groups classified as
healthy and faulty groups with various fault identification
class. The classification accuracy is influenced by the
percentage of data tagged for training and testing.
In previous studies various methods are used to
detect the faults in induction motor from above review the
various faults of motor can be diagnosed. The various
techniques can be implemented by overcome the drawback of
the previous one. The most suitable technique now a days
used are AI techniques. To give better performance of AI
technique, these can be used with signal processing technique
which are also called Hybrid technique. In this paper matlab
simulink software is very useful to implement these
techniques. This will help to design precise model of
induction motor. This model is of practical use which can be
provided with AI system this can trained to model the general
behaviour of a class motor. In the proposed work these
techniques are explained.
III
PROPOSED WORK
IV DISCUSSION
As in [9]y.k.wong has given the modeling of three phase
induction motor using MATLAB simulink. This modeling of
motor is done on the basis of d-q machine model theory.
Simulink model is helpful for faulty data generation, so as
numerical data is generated for training the artificial
intelligence for normal as wel as faulty condition.
But still this dynamic model simulation requires
expertise for mathematical simulation of this d-q model. In
this paper matlab simulink model is used. Instead of
experimental data the training data is generated by this
simulation model that is constructed using the information
found on nameplate of the motor.[5] This simulink model uses
neural network and nerofuzzy system to train the artificial
intelligence for healthy and faulty condition of the motor. This
simulink motor model provided with stator current, voltage,
speed, torque as input. This input parameters are provided in
terms of three cases. Case1 – here input parameter of the
motor is under the consideration of no load for standard
condition that is given on the name plate of the motor. Second
case – is the same input parameters under half load condition.
Third case is for full load condition. After applying this input
parameter case by case we are taking the output from the
simulink motor model, this output from model are stator
current (I), voltage (V), speed (N), and torque (T).
For first case the output parameter will also gives
standard output as per name plate of motor. But for second
case and third case there must be fault will be occur. But for
training the ANN the selection of the best inputs and making
structure compact feature extraction is important. This feature
extraction at different condition that is no load, half load, full
load to collect data. Before this the output from motor model
processed with the FFT algorithm, so as the best input data is
made. The data generated from feature extraction process is
used for training the ANN. The same process is done for
faulty data that is for second and third case condition. When
the ANN trained properly it will provide normal and faulty
decision.
This set up can be test using experimental data of the
induction motor. The experimental data is provided as input to
this set up and depending on this input it will provide the
decision whether the motor is normal or faulty. The motor
used for the following method is 5HP, 440V,1450 rpm.
The matlab simulink based motor
model is proposed. The proposed model uses neural network
and ANFIS network. So as to detect the fault for minimum
experimental data. The proposed model will also compare the
result from neural network and ANFIS. This model will
reduce amount of experimental data that are needed to design
fault detector.
The proposed model is shown as follows:
DSP Processor
(FFT, WT)
Training Set up of
Induction Motor
Data Generation
(Feature Extraction)
ANN (Neural
network, ANFIS)
Testing Set up of
Induction Motor
I
DSP Processor
(FFT, WT)
Data Generation
(Feature Extraction)
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