DWT based Fault Diagnosis of Induction Motor

 

S.M.Shashidhar,Professor & Head, E&CE Dept,

Proudhadevaraya Institute of Technology, Hospet, Karnataka

INTRODUCTION

For several decades now, there have been extensive researches regarding fault detection of induction machines (IMs).

This paper presents a method to detect the inter-turn faults on the stator of three phase squirrel cage induction motor.

• If faults are undetected, they may lead to potentially catastrophic failures.

Stator winding burnt out in motor

winding in stator slots

MCSA based Fault DiagnosisWavelet Transform (WT)

Wavelet Transform (WT) is a potent signal analysis tool that has been employed with success in many areas for nearly two decades.

Wavelets are functions, that satisfy the requirement of both time and frequency localization.

Wavelet can produce multiple resolutions in both time and frequency domain.

Multiresolution Analysis (MRA)

• The MRA offers an effective way to examine the characteristics of a signal at different bands of frequency. This is essential for pattern recognition.

• MRA is well suited for detection and classification of faults in an induction motor.

Multiresolution analysis (MRA) is a new and powerful method of signal analysis well suited to fault generated signals.

The windowing of wavelet transform is adjusted automatically for low and high frequencies.

This gives the wavelet transform much greater compact support for the analysis of the signals with localized transient components.

Discrete Wavelet Transform

The DWT is simply a sampled version of the continuous wavelet transform.

The main idea that underlies the application of discrete wavelet transform (DWT) is the involvements of the successive pairs of high and low pass filters at each scaling stage of wavelet transform.

Filtering Process performed by the DWT

DWT BASED FAULT DETECTION

Capturing The Stator Current

Stator Phase current in case of healthy condition

Stator A phase current in case of faulty condition

Selection of mother wavelet & DWT Decomposition

• There are numerous wavelet families with different mathematical dimensions have been developed.

• Gaussian, Mexican Hat, Meyer, Morlet, Daubechies, Coiflet, etc

• In the field of fault detection of an induction motor, some families have shown more effective results for particular applications..

• The number of decomposition levels is determined by the low-frequency components to be traced.

Fault DiagnosisThe tests were performed in the

laboratory using a squirrel cage motor with four poles, 552 turns per phase winding on the stator, rated 1.5 kW, 415 V, 50Hz. The phase currents were used as the diagnostic signal.

DWT of stator phase current of a healthy Induction Motor

DWT of stator A phase current of a Faulty Induction Motor

SEVERITY OF FAULT

TABLE II

CONCLUSIONFrom the results presented and analysis made, we conclude that the machines subjected to inter-turn short circuit fault can be diagnosed through the characteristic patterns caused by inter-turn short circuit fault components in the DWT analysis by using the stator phase currents.

REFERENCES

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[4] M. Arkan, D. Kostic-Perovic, P. J. Unsworth, “Modelling and Simulation of Induction Motors with Inter-Turn Faults for Diagnostics,” Electric Power Systems Research, vol. 75, pp. 57-66, 2005.

[5] W. Thomson and M. Fenger, “Current Signature Analysis to Detect Induction Motor Faults,” IEEE Ind. Appl. Mag., vol. 7, no. 4, pp. 26–34, 2001.

[6] A. M. D. 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 Envelope,” IEEE Trans. Ind. Electron., vol. 55, issue.3, pp. 13101318, 2008.

[7] Paul C Krause, Oleg Wasynczuk and Scott D Sudoff, Analysis of Electric Machinery and Drive systems, (John Wiley & Sons (ASIA) Pvt. Ltd., 2nd Edition, 2010.