Group-Sparsity Learning Approach for Bearing Fault Diagnosis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Number of pages10
Journal / PublicationIEEE Transactions on Industrial Informatics
Online published8 Oct 2021
Publication statusOnline published - 8 Oct 2021


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.

Research Area(s)

  • 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