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Adaptive wavelet filter based on fractional lower order moment for bearing fault diagnosis

  • Gang Yu*
  • , Xuefeng Zhang
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Wavelet analysis has been widely used in signal denoising or transient signal detection due to its extraordinary time-frequency representation capability. Traditional approach on the selection of parameters for a adaptive wavelet filter was based on higher order statistics that only present limited statistical information about the bearing fault signals. An adaptive wavelet filter based on the Fractional Lower Order Moment (FLOM) of alpha stable distribution is proposed in this paper. The parameters of the Morlet wavelet filter are optimized based on the principle of maximization of FLOM. The diagnosis results based on the the simulated bearing fault signal with low signal to noise ration (SNR) demostrated the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Pages4006-4010
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 3rd International Congress on Image and Signal Processing, CISP 2010 - Yantai, China
Duration: 16 Oct 201018 Oct 2010

Publication series

NameProceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Volume8

Conference

Conference2010 3rd International Congress on Image and Signal Processing, CISP 2010
Country/TerritoryChina
CityYantai
Period16/10/1018/10/10

Keywords

  • Adaptive wavelet analysis
  • Bearing diagnosis
  • Fractional lower order moment
  • Low SNR
  • Morlet wavelet
  • Stable distribution

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