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ISA Trans. 2019 Sep 7. pii: S0019-0578(19)30388-X. doi: 10.1016/j.isatra.2019.08.042. [Epub ahead of print]

Sparsity-assisted bearing fault diagnosis using multiscale period group lasso.

Author information

1
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: anbotao1234@gmail.com.
2
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: zhaozhibin@stu.xjtu.edu.cn.
3
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: wangshibin2008@gmail.com.
4
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: maxvonshawn@stu.xjtu.edu.cn.
5
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: chenxf@mail.xjtu.edu.cn.

Abstract

Fault diagnosis methods based on sparse representation (SR) theory have achieved great success recently. However, it is still challenging to extract features from signals with strong noise interference. In this paper, a new method named multiscale period group Lasso (MPG Lasso) is proposed, which is a multiscale SR model performed in different wavelet subbands and uses nonconvex regularization functions to enhance sparsity within and across groups. The improvements of this method over conventional SR models are mainly made in the three aspects. First, impulses are extracted in the wavelet domain which is likely to produce more sparse results. Second, a multiscale periodic prior is embedded within the penalty function to make the extraction of characteristic frequency more accurate. Third, a rule of adaptive hyper-parameter setting in the model is further studied to simplify the industrial application of MPG Lasso. Performance of MPG Lasso is verified by a series of simulation experiments and diagnosis of fault bearings. The results show that MPG Lasso gets better performance than other up-to-date methods.

KEYWORDS:

Multiscale denoising; Parameter self-adaptation; Sparse representation; TQWT

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