 Open Access
 Authors : Ch. Sridhar, N. Rama Devi
 Paper ID : IJERTCONV8IS16041
 Volume & Issue : NCSMSD – 2020 (Volume 8 – Issue 16)
 Published (First Online): 18102020
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Detection and Discrimination of Bearing Faults of A Three Phase Induction Motor from Single Phasing Faults using Wavelet Transform
Ch. Sridhar
Department of EEE Acharya Nagarjuna University,
Guntur, India.
N. Rama Devi
Department of Electrical and Electronics Engineering, Bapatla Engineering College,
Bapatla, India
Abstract This paper describes a protection scheme for a 3phase induction motor to detect and discriminate the bearing faults from single phasing faults. The loyalty of an Asynchronous induction motor is of predominant importance in various applications like industrial, commercial, aerospace and military. Rotating mechanical part bearings played an important role in the reliability and performance of all motor systems. In this paper the 3phase stator current signals are captured in P.C using DIP8000 that is having a sample frequency of 5.3KHZ. These signals are analyzed with Bior5.5 wavelet and decomposed up to 5th level. The values of d1 coefficient is above the predefined value of Threshold1 (tp) then the motor is under fault. In case of bearing faults all these values are above the predefined threshold otherwise it is healthy. The phase which is above Tp represents the single phasing fault on that phase.
Index Terms: Bearing fault, sampling frequency, Threshold and Wavelet Transform.

INTRODUCTION
AC rotating Electrical motors especially induction motors are used for various industrial utilities because it plays a noninterchangeable role in many of the industrial processes [1]. Those asynchronous induction motors possess a variety of features those are not compatible with other motors like low cost, reliability and ruggedness but still those motors are devoted to some failures due to malfunctioning and manufacturing defects. Hence condition monitoring is necessary in order to reduce the cost of production [2].In rotating machinery bearing is one of the significant mechanical rolling part and it has expansive industrial and domestic applications proper function depends on the smooth
operation performed by the bearings [34].Hence early fault detection is not done those faults are gradually degrade the performance of machine and also leads to motor interruption.

FAULTS CLASSIFICATION
Induction motor during running condition subjectected to various faults like electrical as well as mechanical faults.The fig.1 shows the percentage of various faults distribution shows in the pie diagram. Bearing faults are mechanical faults accounting for most motor failures.
Fig.1 Pie diagram for various faults distribution
Similarly in the recent past several research studies shows that major category of faiulers in induction motor are bearing faults.The following below table I confer various research surveys performed by Electric Power Research Institute (EPRI), surveyed 6312 motors[5] and the reliability Working Group of motor of the IEEEIAS, surveyed 1141 working machines [6].
It is clear from the above table shows that the prevailing causes of the machines failures are mechanical rotating part bearing faults are major contribution. For significant fault detection various research studies gives out the least expensive bearing faults to fix the problem and at the same time it is most challenging to detect the fault under different operating conditions.
SINGLE PHASING FAULTS
Singlephasing means nothing but opening of one of the any threephases. It leads to unbalance in the voltage. If a single phasing occurs in 3phase motor full horse power will be delivered that is enough for driving the load. The motor continues to drive the load till it may burns out or until properly sized dual element and over load elements. The motor will be off from the line with the help of time delaying fuses. In the
case of lightly loaded motors the phase currents increased by square root of three (3) that secondary singlephasing conditions. This leads to draw an over current of 20% more than the fullload value. The motor may be damaged by the circulating currents if the overloads are sized at 125% of the rotating condition. Hence it is more appropriate to protect the motor against over loads rather than against the rated current.
In order to detect and classify the various faults several signal processing techniques involves the electrical machines data collection and the available data is processed by using several fault detection techniques. The rotating electrical machine condition monitoring is tested with the raw signal, those data collected by through various supportive equipment and sensors. The fault feature extraction done by comparing healthy case to faulty case by choosing a suitable signal processing technique. Earlier a several number of data acquisition approaches have been established for collection of certain parameters of electrical machines.
In most of the condition monitoring techniques the motors operating under different loaded conditions, for analyzing the fault feature the captured raw current with signal contaminated with noise, nonstationary condition. Hence it is very difficult to analyze the signal in the timedomain[7].For recent years advanced powerful signal processing techniques available analyze the signal in timefrequency domain for identification of faults[8] and also other noninvasive vibration and motor current signature analysis are used for condition monitoring of bearings[9],especially the following four methods frequency, time domain[1011],enhanced frequency[1213] and timescale[1415] signal processing methods are used to extract the fault feature of the bearing.
WAVELET TRANSFORM
For the analyzation of a signal in both frequency and time domains, the wavelet is found to be a powerful mathematical tool. A given signal is split into a number of signals corresponds to different frequency bands. These splitted signals store information more efficiently than of Fourier transform.Varites of wavelets are used for condition monitoring of electrical machines out of them Discrete wavelet transform (DWT) with multiresolution analysis is used frequently. It provides a signal information in timescale domain. This transform is an ideal tool analyzing the signal in nonstationary and transient nature. The continuous wavelet transform (CWT) of a given function f(t) gives the approach in timescale domain used for identified as overall sum of the signal is the product of shifted and scaled version of the mother wavelet function (t).Those function was given by Smith and McFadden as follows below
(1)
Here the parameters a represented as scale index, it is a reciprocal of frequency and the parameter b shows the translation or tmeshifting. The transform is derived from the discretization of CWT (a,b) and those function was proposed by Smith and McFadden as follows below
(2)
here a and b replaced with by 2j and 2jk. In 1989 Mallat developed an efficient method using filters. The signal f(t) is passed through by two complementary filters and appears as low and high frequency signals. The signals are further broken into lower and higher resolution with successive approximations. The following fig.2 explains the Dyadic wavelet decomposition algorithm
Fig.2. Dyadic wavelet decomposition algorithm

EXPERIMENTAL SETUP AND DATA ACQUISITION
A 3HP, 3 phase, 4 pole, 415 volts, 4.8A, 50Hz Induction motor is used for various bearing faults detection. The stator currents under various bearings faults condition are captured with UNIPOWER DIP8000 power network analyser with a measuring capacity of 5300 samples pr second. After that export and import of those signals with a PC inter faced RS 232 port and complete the analysis with MATLAB software. The fig.3 and fig.4 shows the pictorial representation of Induction motor with UNIPOWER DIP8000 respectively.
Fig.3. Experimental test bench
Fig.4 Power network analyzer
The following fig.5,fig.6,and fig.7shows the pair of healthy bearings, pair of outer race defect bearings and pair ball defect bearings respectively used for done the bearing faults.
Fig.5. Pair of healthy bearings.
Fig.6. Pair of faulty bearings (Inner and outer race defect)
Fig.7. pair of faulty bearings (ball defect)

EXPERIMENTAL RESULTS
The Fig.8 shows the 3phase currents of the induction motor, which are captured from the Dip 8000(power network analyzer). These currents are analyzed with bior5.5 to obtain the d1 coefficients.Fig.8 represents the variation of d1 coefficients with respect to time for healthy bearings. The sum of absolute value of d1 coefficients are compared for all the phase currents with predefined Threshold1(1.16) All these values are below the threshold1 hence the motor is in healthy condition.
A.HEALTHY BEARING:
(a)
(b)
(c)
absolute values of d1 coefficients are compared for all the phase currents of Inner race defect with predefined Threshold1 as illustrated in fig 10 (b). Similarly Inner race defect in Rphase and BPhase cases also the values are above the threshold1 (1.16), hence the motor bearings are faulty.
(d)
(e)
(f)
Fig,8(a),(b),(c),(d),(e) and (f) shows the variation of 3phase Currents R,Y and B and ther d1 coeffient varion respectively.

BALL DEFECT:
The following Fig 9. represents the Rphase current in the 3phase induction motor with Ball defect for which are captured from the Dip 8000(power network analyzer). These currents are analyzed with bior5.5 to obtain the d1 coefficients. The sum of absolute value of d1 coefficients are compared for all the phase currents of Ball defect with predefined Threshold1 as illustrated in fig.9 All these values are above the threshold1 (1.16) hence the motor bearings are faulty. Similarly Ball defect in Yphase and BPhase also verified .
(a)
(b)
Fig,10 (a) and (b) shows the variation of YPhase current and ther d1 coeffient varion respectively.
CLASSIFICATION OF BEARING FAULTS:
Rphase Currents:
The captured current signals are analyzed by Wavelet analysis. The energy value of 5th level detail coefficients of three phase currents are tabulated as follows. Energy value of 5th level detail coefficients of three phase normal & faulty currents in R phase shown in Fig.11, fig.12 and Fig.13 respectively. The detection of faulty phase can be analyzed by comparing the energy value of 5th level detail coefficients of three phase currents are compared with a predefined threshold to identify the faulty phase. The energy values of d1 coefficients are high compared to d2, d3, d4, d5. All the values are shown in table1,2&3.
Table1: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in R phase
Level
Healthy Bearing Current I1
Ball defect Bearing Current I1
Inner race defect Bearing Current I1
Single Phasing Current I1
Threshold
1
1.16
3.5314
1.2653
7.0777
1.16
2
0.6652
0.7563
0.4638
2.0649
1.16
3
0.338
0.2945
0.2439
0.5675
1.16
4
0.3219
0.2379
0.1351
0.3144
1.16
5
0.2244
0.771
0.7111
0.9201
1.16
Level
Healthy Bearing Current I1
Ball defect Bearing Current I1
Inner race defect Bearing Current I1
Single Phasing Current I1
Threshold
1
1.16
3.5314
1.2653
7.0777
1.16
2
0.6652
0.7563
0.4638
2.0649
1.16
3
0.338
0.2945
0.2439
0.5675
1.16
4
0.3219
0.2379
0.1351
0.3144
1.16
5
0.2244
0.771
0.7111
0.9201
1.16
(a)
(b)
Fig,9(a) and (b) shows the variation of RPhase current and ther d1 coeffient varion respectively.

INNER RACE DEFECT:
The Fig.10 (a) shows the Yphase stator current variation for inner race defect bearing which are captured from the Dip 8000(power network analyzer). These currents are analyzed with bior5.5 to obtain the d1 coefficients. Fig.10 (a) represents the variation of d1 coefficients with respect to time. The sum of
Fig.11 Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in R phase
Table2: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in R phase
Level 
Healthy Bearing Current I2 
Ball defect Bearing Current I2 
Inner race defect Bearing Current I2 
Single Phasing Current I2 
Threshold 
1 
0.4663 
2.9738 
6.2699 
10.9285 
1.16 
2 
0.2525 
0.8928 
2.6059 
3.2018 
1.16 
3 
0.1315 
0.3121 
1.1876 
1.0901 
1.16 
4 
0.2413 
0.1716 
0.7112 
0.5352 
1.16 
5 
0.3398 
0.554 
0.9168 
0.7907 
1.16 
Fig.12 Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in R phase
Table3: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in R phase
Level 
Healthy Bearing Current I3 
Ball defect Bearing Current I3 
Inner race Bearing defect Current I3 
Single phasing Current I3 
Threshold 
1 
0.4704 
2.1582 
2.4054 
9.8616 
1.16 
2 
0.2933 
0.7412 
0.6093 
2.9722 
1.16 
3 
0.1483 
0.2364 
0.3455 
0.8916 
1.16 
4 
0.1887 
0.1329 
0.3532 
0.3836 
1.16 
5 
0.237 
0.6501 
0.8227 
1.045 
1.16 
Fig.13. Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in R phase
Yphase Currents:
The captured current signals are analyzed by Wavelet analysis. The energy value of 5th level detail coefficients of three phase currents are tabulated as follows. Energy value of 5th level detail coefficients of three phase normal & faulty currents in Yphase shown in Fig.14, fig.15 and Fig.16 respectively. The detection of faulty phase can be analyzed by comparing the energy value of 5th level detail coefficients of three phase currents are compared with a predefined threshold to identify the faulty phase. The energy values of d1
coefficients are high compared to d2, d3, d4, d5. All the values are shown in table4, 5 & 6.
Table4: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in Yphase
Level 
Healthy Bearing Current I1 
Ball defect Bearing Current I1 
Inner race Bearing defect Current I1 
Single phasing Current I1 
Threshold 
1 
1.16 
3.5314 
1.2653 
4.4779 
1.16 
2 
0.6652 
0.7563 
0.4638 
1.3638 
1.16 
3 
0.338 
0.2945 
0.2439 
0.4316 
1.16 
4 
0.3219 
0.2379 
0.1351 
0.1938 
1.16 
5 
0.2244 
0.771 
0.7111 
0.1638 
1.16 
Fig.14 Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in Y phase
Table5: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in Yphase
Level 
Healthy Bearing Current I2 
Ball defect Bearing Current I2 
Inner race Bearing defect Current I2 
Single phasing Current I2 
Threshold 
1 
0.4663 
2.9738 
6.2699 
17.7159 
1.16 
2 
0.2525 
0.8928 
2.6059 
4.6711 
1.16 
3 
0.1315 
0.3121 
1.1876 
1.4521 
1.16 
4 
0.2413 
0.1716 
0.7112 
0.4636 
1.16 
5 
0.3398 
0.554 
0.9168 
0.4392 
1.16 
Fig.15. Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in Y phase
Table6: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in Yphase
Level 
Healthy Bearing Current I3 
Ball defect Bearing Current I3 
Inner race Bearing defect Current I3 
Single phasing Current I3 
Threshold 
1 
0.4704 
2.1582 
2.4054 
9.0567 
1.16 
2 
0.2933 
0.7412 
0.6093 
1.9296 
1.16 
3 
0.1483 
0.2364 
0.3455 
0.7196 
1.16 
4 
0.1887 
0.1329 
0.3532 
0.2799 
1.16 
5 
0.237 
0.6501 
0.8227 
1.0775 
1.16 
Fig.16. Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in Y phase
Bphase Currents:
The captured current signals are analyzed by Wavelet analysis. The energy value of 5th level detail coefficients of three phase currents are tabulated as follows. Energy value of 5th level detail coefficients of three phase normal & faulty currents in Bphase shown in Fig.17,fig.18 and Fig.19 respectively. The detection of faulty phase can be analyzed by comparing the energy value of 5th level detail coefficients of three phase currents are compared with a predefined threshold to identify the faulty phase. The energy values of d1 coefficients are high compared to d2, d3, d4, d5. All the values are shown in table7,8 and 9.
Table7: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in Bphase
Level 
Healthy Bearing Current I1 
Ball defect Bearing Current I1 
Inner race Bearing defect Current I1 
Single phasing Current I1 
Threshold 
1 
1.16 
3.5314 
1.2653 
6.392 
1.16 
2 
0.6652 
0.7563 
0.4638 
1.6718 
1.16 
3 
0.338 
0.2945 
0.2439 
0.5564 
1.16 
4 
0.3219 
0.2379 
0.1351 
0.2315 
1.16 
5 
0.2244 
0.771 
0.7111 
1.0396 
1.16 
Fig.17. Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in B phase
Table8: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in Bphase
Level 
Healthy Bearing Current I2 
Ball defect Bearing Current I2 
Inner race Bearing defect Current I2 
Single phasing Current I2 
Threshold 
1 
0.4663 
2.9738 
6.2699 
14.7018 
1.16 
2 
0.2525 
0.8928 
2.6059 
4.0878 
1.16 
3 
0.1315 
0.3121 
1.1876 
1.3547 
1.16 
4 
0.2413 
0.1716 
0.7112 
0.4661 
1.16 
5 
0.3398 
0.554 
0.9168 
1.009 
1.16 
Fig.18. Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in B phase
Table9: Energy values of 5th level detail coefficients of 3ph induction motor under healthy &faulty conditions in Bphase
Level 
Healthy Bearing Current I3 
Ball defect Bearing Current I3 
Inner race Bearing defect Current I3 
Single phasing Current I3 
Threshold 
1 
0.4704 
2.1582 
2.4054 
22.7754 
1.16 
2 
0.2933 
0.7412 
0.6093 
6.2595 
1.16 
3 
0.1483 
0.2364 
0.3455 
1.7263 
1.16 
4 
0.1887 
0.1329 
0.3532 
0.6073 
1.16 
5 
0.237 
0.6501 
0.8227 
0.314 
1.16 
Fig.19. Energy value of 5th level detail coefficients of 3ph induction motor under healthy & faulty conditions in B phase
From the above analysis the energy value of 5th level detailed coefficients of three phase currents are used to identify the faulty phase. The values of d1 coefficients of normal and fault conditions compared with predefined threshold. At d1 coefficient identify the fault compared to remaining coefficient values. All these values are above the threshold hence the motor bearings are faulty.
SINGLE PHASING FAULT:
The Fig. 20,22,and fig.24 shows the 3phase currents of the induction motor of R,Y and Bphases of single phasing phaseR, which are captured from the Dip 8000(power network analyzer). These currents are analyzed with bior5.5 to obtain the d1 coefficients. The Fig. 21,23,and fig.25 shows variation
of d1coefficients with respect to time for single phasing fault for R,Y and B Phases respectively. Similarly in the remaing two phases Y and Bphases validated results are verified.
Single phasing Phase R:
Fig.20 Variation of RPhase Current in singlephasing R
Fig.21 Variation of d1 coefficients of Rphase in Singlephasing R
Fig.22 Variation of YPhase Current in singlephasing R
Fig.23 Variation of d1 coefficients of Yphase in Singlephasing R
Fig.24 Variation of BPhase Current in singlephasing R
Fig.25 Variation of d1 coefficients of Bphase in Singlephasing R
CONCLUSION
This paper introduces wavelet decomposition method for analyzing the transients of threephase induction motor line currents. This scheme effectively detects and discriminate the type of fault(i.e. bearing fault or single phasing fault) by comparing the d1 coefficients with a predefined threshold . This scheme also classifies the bearing faults effectively by comparing the values of d5coefficients with predefined threshold .The energy value of 5th level detailed coefficients of three phase currents are used to identify the faulty phase. This proposed scheme is fast and reliable to detect and classify most common bearing fault from single phasing fault.
REFERENCES

Didier G, Ternisien E, Caspar O, Razik H. A new approach to detect broken rotor bars in induction machines by current spectrum analysis. Mechanical Systems and Signal Processing. 2007; 21: 11271142.

Chow MY. Guest editorial special section on motor fault detection and diagnosis. IEEE Transactions on Industrial Electronics. 2000; 47.5: 982983.

Yan R. and Gao R X 2009 Base Wavelet Selection for Bearing Vibration Signal Analysis Int. J. Wavelets Multiresoluton & Inf. Process 7(4) 41126

Tandon N and A Choudhury 1999 A Review of Vibration and Acoustic Measurement Methods for The Detection of Defects in Rolling Element Bearings Tribolgy International 46980.

P. D. McFadden and J. D. Smith, "Model for the vibration produced by a single point defect in a rolling element bearing," Journal of Sound and Vibration, vol. 96, no. 1, pp. 6982, 1984.

Y.T. Su and S.J. Lin, "On initial fault detection of a tapered roller bearing: Frequency domain analysis," Journal of Sound and Vibration, vol. 155, no. 1, pp. 7584, 1992.

Bin GF, Gao JJ, Li XJ, Dhillon BS. Early fault diagnosis of rotating machinery based on wavelet packets Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing. 2012; 27: 696711.

Burnett R, Watson J, Elder S. The application of modern signal processing techniques to rotor fault detection and location within three phase induction motors. Signal processing. 1996; 49.1: 5770.

R. B. Randall, VibrationBased ConditionMonitoring: Industrial,Aerospace and Automotive Applications, John Wiley & Sons,Chichester, UK, 2011.

J. PonsLlinares, J. A. AntoninoDaviu, M. RieraGuasp, S. B. Lee, T.J. Kang, and C. Yang, "Advanced induction motor rotor fault diagnosis via continuous and discrete timefrequency tools," IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 17911802, 2015.

M. E. H. Benbouzid, M. Vieira, and C. Theys, "Induction motors' faults detection and localization using stator current advanced signal processing techniques," IEEE Transactions on Power Electronics, vol. 14, no. 1, pp. 1422, 1999.

L. Eren and M. J. Devaney, "Bearing damage detection via wavelet packet decomposition of the stator current," IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 2, pp. 431436, 2004.

Z. Ye, B. Wu, and A. Sadeghian, "Current signature analysis of induction motor mechanical faults by wavelet packet decomposition," IEEE Transactions on Industrial Electronics, vol. 50, no. 6, pp. 12171228, 2003.

S. Prabhakar, A. R. Mohanty, and A. S. Sekhar, "Application of discrete wavelet transform for detection of ball bearing race faults," Tribology International, vol. 35, no. 12, pp. 793800, 2002.

H. R. Cao, F. Fan, and K. Zhou, Z. J. He, "Wheelbearing fault diagnosis of trains using empirical wavelet transform," Measurement, vol. 82, pp. 439449, 2016.
AUTHORS PROFILE
Ch. Sridhar received the B.Tech. degree in electrical and electronics engineering from
J.N.T.U. Engineering College, Anantapur, India in 2002, and the M.Tech degree in Electrical power Engg from the J.N.T.U. Engineering College, Hyderabad, India in 2007.He is working as Asst.Professor in the Department of Electrical and
Electronics Engineering at Bapatla Engineering College, Bapatla,India since 2006. Presently, He is pursuing Ph.D. degree at the Acharya Nagarjuna University, Guntur, India.
N. Rama Devi received the B.Tech. Degree in electrical and electronics engineering from J.N.T.U. Engineering College, Kakinada, India in 1997, and the M.Tech degree in power systems from the Regional Engineering College, Warangal, India, in 2000. She received his doctorate degree from the National Institute of Technology, Warangal, India in
2018. Presently, she is working as a Professor and Head of the Department of Electrical & Electronics Engineering at Bapatla Engineering College, Bapatla, India. Her areas of interest condition monitoring of AC motors and Artificial Intelligence.