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 language | English |
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Number of pages | 10 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 18 |
Issue number | 7 |
Online published | 8 Oct 2021 |
DOIs | |
Publication status | Published - 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