Paper Title:
Wavelet Grey Moment Vector and Hidden Markov Model Based Fault Diagnosis for Ball Bearing
  Abstract

The paper introduces a new approach to detect the fault of bearing based on wavelet grey moment vector and hidden Markov modeling (HMM). Because of non-stationary characteristics of vibration signals of faulty bearings, we propose a new method to extract the wavelet grey moment vectors from these signals. The grey moment vectors are used as feature parameters to train HMMs to establish the database. Fault modes of bearings can be identified by select the HMM with the highest probability. The experimental results show that the proposed approach is effective and accurate to detect the faulty bearing for every single fault.

  Info
Periodical
Chapter
Chapter 2: Materials Science in Computer-Aided Manufacturing and Design
Edited by
Wensong Hu
Pages
210-215
DOI
10.4028/www.scientific.net/AMR.346.210
Citation
J. P. Xuan, Z. B. Xu, B. Wu, T. L. Shi, "Wavelet Grey Moment Vector and Hidden Markov Model Based Fault Diagnosis for Ball Bearing", Advanced Materials Research, Vol. 346, pp. 210-215, 2012
Online since
September 2011
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Price
$32.00
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