Group-Sparsity Learning Approach for Bearing Fault Diagnosis

Jisheng Dai*, Hing Cheung So

*Corresponding author for this work

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

27 Citations (Scopus)

Abstract

Fault impulse extraction under strong background noise and/or multiple interferences is a challenging task for bearing fault diagnosis. Sparse representation has been widely applied to extract fault impulses and can achieve state-of-the-art performance. However, most of the current methods rely on carefully tuning several hyper-parameters and suffer from possible algorithmic degradation due to the approximate regularization and/or heuristic sparsity model. To overcome these drawbacks, we present a sparse Bayesian learning (SBL) framework for bearing fault diagnosis, and then propose two group-sparsity learning algorithms to extract fault impulses, where the first one exploits the group-sparsity of fault impulses only; while the second one utilizes additional periodicity behavior of fault impulses. Due to the inherent learning capability of the SBL framework, the proposed algorithms can tune hyper-parameters automatically and do not require any prior knowledge. Another advantage is that our solutions are maximum a posteriori estimators in the sense of Bayesian optimality, which can yield higher accuracy. Results on both simulated and real datasets demonstrate the superiority of the developed algorithms.
Original languageEnglish
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number7
Online published8 Oct 2021
DOIs
Publication statusPublished - Jul 2022

Research Keywords

  • Bayes methods
  • Bearing fault diagnosis
  • Fault diagnosis
  • group-sparsity
  • Heuristic algorithms
  • Periodic structures
  • quasi-periodicity
  • Signal processing algorithms
  • sparse Bayesian learning
  • sparse representation
  • Tuning
  • variational Bayesian Inference
  • Vibrations

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