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Fault diagnosis of high-speed rolling element bearings using wavelet packet transform

Fault diagnosis of high-speed rolling element bearings using wavelet packet transform The time-frequency analysis techniques like Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and wavelet packet analysis have been compared to detect and diagnose faults in rotor bearing system. Discrete Wavelet Transform (DWT) provides flexible time frequency resolution which suffers from a relatively low resolution in the highfrequency region. This deficiency leads to difficulty in differentiating high-frequency transient components. WPT based signal decomposition process up to n-level produces a total of 2n subbands, with each sub-band covering 1/2n of the signal frequency spectrum. WPT based global threshold criterion is applying before denoising of detail information. This denoised signal is then auto correlate with original signal and energy spectrum is generated for diagnosis of bearing fault. The enhanced signal decomposition capability makes WPT an attractive tool for detecting and differentiating transient elements with high-frequency characteristics and helping in the minimisation of interventions by the end user. Keywords: fault diagnosis; wavelet transforms; CWT; WPT; transient vibration signal. Reference to this paper should be made as follows: Pandya, D.H., Upadhyay, S.H. and Harsha, S.P. (2015) ` using wavelet packet transform', Int. J. Signal and Imaging Systems Engineering, Vol. 8, No. 6, pp.390­401. Biographical notes: Divyang H. Pandya completed PhD (Vibration & http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Fault diagnosis of high-speed rolling element bearings using wavelet packet transform

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2015.072922
Publisher site
See Article on Publisher Site

Abstract

The time-frequency analysis techniques like Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and wavelet packet analysis have been compared to detect and diagnose faults in rotor bearing system. Discrete Wavelet Transform (DWT) provides flexible time frequency resolution which suffers from a relatively low resolution in the highfrequency region. This deficiency leads to difficulty in differentiating high-frequency transient components. WPT based signal decomposition process up to n-level produces a total of 2n subbands, with each sub-band covering 1/2n of the signal frequency spectrum. WPT based global threshold criterion is applying before denoising of detail information. This denoised signal is then auto correlate with original signal and energy spectrum is generated for diagnosis of bearing fault. The enhanced signal decomposition capability makes WPT an attractive tool for detecting and differentiating transient elements with high-frequency characteristics and helping in the minimisation of interventions by the end user. Keywords: fault diagnosis; wavelet transforms; CWT; WPT; transient vibration signal. Reference to this paper should be made as follows: Pandya, D.H., Upadhyay, S.H. and Harsha, S.P. (2015) ` using wavelet packet transform', Int. J. Signal and Imaging Systems Engineering, Vol. 8, No. 6, pp.390­401. Biographical notes: Divyang H. Pandya completed PhD (Vibration &

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2015

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