Paper Title:
Application of Wavelet Packet Transform for Detection of Ball Bearing Race Fault
  Abstract

In this study, a fault diagnosis system is proposed for rolling ball bearing race using wavelet packet transform(WPT) and artificial neural network(ANN)technique. Vibration signal from ball bearings having defects on inner race and outer race is considered and the extraction method of feature vector based on wavelet packet transform with frequency band energy is used. The vibration signal is decomposed into the individual frequency bands. The variations of the signal energy in these bands reflect the different fault locations. Further, the artificial neural network is proposed to develop the diagnostic rules of the data base in the present fault identification system. The experimental work is performed to evaluate the effect of fault diagnosis in a rolling ball bearing platform under different fault conditions. The experimental results indicate the effectiveness of the proposed method in fault bearing identification.

  Info
Periodical
Materials Science Forum (Volumes 626-627)
Edited by
Dongming Guo, Jun Wang, Zhenyuan Jia, Renke Kang, Hang Gao, and Xuyue Wang
Pages
511-516
DOI
10.4028/www.scientific.net/MSF.626-627.511
Citation
D. Y. Wang, W. Z. Zhang, W. P. Lu, J.W. Du, "Application of Wavelet Packet Transform for Detection of Ball Bearing Race Fault", Materials Science Forum, Vols. 626-627, pp. 511-516, 2009
Online since
August 2009
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Price
$32.00
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