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Wavelet based Fault Classification for Rolling Element Bearing in Induction Machine

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 12
Year of Publication: 2014
Amit Shrivastava
Sulochana Wadhwani

Amit Shrivastava and Sulochana Wadhwani. Article: Wavelet based Fault Classification for Rolling Element Bearing in Induction Machine. International Journal of Computer Applications 90(12):17-19, March 2014. Full text available. BibTeX

	author = {Amit Shrivastava and Sulochana Wadhwani},
	title = {Article: Wavelet based Fault Classification for Rolling Element Bearing in Induction Machine},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {12},
	pages = {17-19},
	month = {March},
	note = {Full text available}


Induction motors plays the most important role in any industry. Induction motor faults results in motor failure causing breakdown and great loss of production due to shutdown of industry and also increases the running cost of machine with reduction in efficiency. This needs for early detection of fault with diagnosis of its root cause. In this research paper a wavelet based fault classification method has been developed for rolling element bearing in induction motor using vibration signal. Wavelet based vibration analysis is one of the most successful techniques used for condition monitoring of rotating machines. This paper describes a new condition monitoring method for induction motors based on wavelet transform. A robust bearing fault detection scheme has been developed by time-frequency domain feature extraction from vibration signals of healthy and defective machine.


  • Renwick J. T. 1984 Condition Monitoring of Machinery Using Computerised Vibration Signature Analysis. IEEE Trans. On Industry Applications, 519-527.
  • Thomson W. T. , 2001 Current Signature Analysis to Detect Induction Motor Faults, IEEE Industry Applications Magazine, (July/Aug. 2001), 26-34.
  • Kliman G. B. , Premerland W. J. , Koegl R. A. , and Hoeweler D. , 1996 A New Approach to On-Line Turn Fault Detection in AC Motors, in Conf. Proec. IEEE IAS'96, Vol. 1, San Diego, CA, USA, 687-693.
  • Altmann J. , Mathew J. , 2001 Multiple Band-Pass Autoregressive Demodulation for Rolling Element Bearing Fault Diagnosis, Mechanical Systems and Signal Processing, Vol 15, No. 5, 963-977.
  • Tse P. W. , Peng Y. H. , Yam Richard, July 2001 Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis: Their Effectiveness and Flexibilities, Journal of Vibration and Acoustics, Vol. 123, No. 3, pp 303-310.
  • Silva A. A. Da et al. ,(1997) Rotating Machinery Monitoring and Diagnosis using Short Time Fourier Transform and Wavelet Techniques, in Proc. International Conference Maintenance & Reliability, Vol. 1, Knowville, TN, 14. 01-14. 15.
  • Shrivastava Amit, Wadhwani Sulochana Vibration Signature Analysis for Ball Bearing of Three Phase Induction Motor, International Journal of Electrical and Electronics Engineering (IOSRJEEE). Volume 1, Issue 3, July-August 2012, 46-50.