Paper Title:
Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)
  Abstract

This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and to generate feature vectors. GMMs were trained and used as a diagnostic classifier. Experimental results have shown that GMMs can reliably classify different fault conditions and have a better classification performance as compared to the multilayer perceptron neural networks.

  Info
Periodical
Edited by
Kai Cheng, Yingxue Yao and Liang Zhou
Pages
553-557
DOI
10.4028/www.scientific.net/AMM.10-12.553
Citation
J. Sun, G. Yu, C. N. Li, "Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)", Applied Mechanics and Materials, Vols. 10-12, pp. 553-557, 2008
Online since
December 2007
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