A fault diagnosis method of rolling bearing based on improved deep residual shrinkage networks. (January 2023)

• Record Type: Journal Article
• Authors: Tong, Jinyu; Tang, Shiyu; Wu, Yi; Pan, Haiyang; Zheng, Jinde
• Is Part Of: Measurement. Volume 206(2023)
• Publication Date: 2023-01
• Subjects: Fault diagnosis -- Rolling bearing -- Deep residual shrinkage networks -- Pseudo-soft threshold function -- Adaptive slope block; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112282
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This article constructs the IDRSN for fault diagnosis under noise backgrounds. The PSTB and ASB use attention mechanism to infer self-adaptive parameters. The IPSTF solves the problem of signal distortion and gradient disappearance. The method can achieve better fault diagnosis performance under noise backgrounds. Abstract: Aiming at the problem of signal distortion caused by deep residual shrinkage network (DRSN) in the noise reduction process, improved deep residual shrinkage network (IDRSN) are proposed and applied to rolling bearing fault diagnosis under noise backgrounds. Firstly, we design an improved pseudo-soft threshold function (IPSTF) to eliminate the signal distortion caused by the soft threshold function(STF). Then, a pseudo-soft threshold block (PSTB) and an adaptive slope block (ASB) are proposed to construct an improved residual shrinkage building unit (IRSBU) for setting the optimal threshold and slope adaptively. Finally, the method is applied to rolling bearing fault diagnosis in two different operating conditions under noise backgrounds. The results show that the proposed method has higher accuracy and robustness than the existing methods.