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Wavelet based Fault Classification for Rolling Element Bearing in Induction Machine
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 90 - Number 12 |
| Year of Publication: 2014 |
| Authors: Amit Shrivastava, Sulochana Wadhwani |
10.5120/15772-4179
|
Amit Shrivastava, Sulochana Wadhwani . Wavelet based Fault Classification for Rolling Element Bearing in Induction Machine. International Journal of Computer Applications. 90, 12 ( March 2014), 17-19. DOI=10.5120/15772-4179
Abstract
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.
References
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