Flexible Analytical Wavelet Transform Enhanced Sparse Representation with Nonconvex Penalty and Its Application to Weak Fault Feature Extraction of Rolling Bearings

Keshen Cai, Chunlin Zhang, Wenbo Hou, Yadong Feng, Fangyi Wan

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

Abstract

To address the problem of early-stage weak fault feature extraction of rolling bearings, non-convex penalty enhanced sparse representation method based on flexible analytical wavelet transform is proposed. The flexible analytical wavelet transform possesses tunable time-frequency frame and atom oscillation, and optimal flexible analytical wavelet transform frame is selected to match the impulsive fault feature to be extracted. Further, sparse representation model based on generalized min-max concave penalty is established, in which the non-convex penalty could induce sparsity of the solutions and the optimization function could remain convex. Thus, the forward-backward splitting method is adopted for solving the optimization problem of sparse representation model.

Original languageEnglish
Title of host publication2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1268-1272
Number of pages5
ISBN (Electronic)9798350339994
DOIs
StatePublished - 2023
Event6th International Conference on Information Communication and Signal Processing, ICICSP 2023 - Xi'an, China
Duration: 23 Sep 202325 Sep 2023

Publication series

Name2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023

Conference

Conference6th International Conference on Information Communication and Signal Processing, ICICSP 2023
Country/TerritoryChina
CityXi'an
Period23/09/2325/09/23

Keywords

  • flexible analytical wavelet transform
  • non-convex penalty enhanced sparse representation
  • rolling bearings
  • weak fault feature extraction

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