Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

22 Scopus Citations
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  • Dong Wang
  • Kwok-Leung Tsui
  • Qiang Zhou


Original languageEnglish
Pages (from-to)80-91
Journal / PublicationMechanical Systems and Signal Processing
Online published30 Nov 2015
Publication statusPublished - May 2016


Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

Research Area(s)

  • Bearing fault diagnosis, Gauss-Hermite integration, Kurtogram, Bayesian inference, Wavelet transform, NOISE-REDUCTION METHOD, EXTRACTION, SELECTION, FEATURES, FILTER