Improved deep residual shrinkage network used for bearing fault diagnosis

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (18) : 217-224.

TANG Shiyu1,TONG Jinyu1,2,ZHENG Jinde1,PAN Haiyang1,WU Yi1

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Abstract

Aiming at the signal distortion problem caused by deep residual shrinkage network (DRSN) in the noise reduction process, an improved deep residual shrinkage network (IDRSN) is proposed and applied to the fault diagnosis of rolling bearing. Firstly, an improved semi-soft threshold function (ISSTF) was introduced to solve the problem of identity deviation and eliminate the signal distortion caused by the soft threshold function. Then, a semi-soft threshold block (SSTB) and an adaptive slope block (ASB) were designed to construct an improved residual shrinkage building unit (IRSBU), which was used to adaptively set the optimal threshold value and further correct the output. Finally, the proposed method was applied to fault diagnosis of rolling bearing in two different operating conditions. The results showed that the proposed method had higher classification accuracy and robustness compared with the existing methods, and was more effective for fault diagnosis under variable speed conditions.

Key words

fault diagnosis / rolling bearing / deep residual shrinkage network / semi-soft threshold function / adaptive slope block

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TANG Shiyu1,TONG Jinyu1,2,ZHENG Jinde1,PAN Haiyang1,WU Yi1. Improved deep residual shrinkage network used for bearing fault diagnosis[J]. Journal of Vibration and Shock, 2023, 42(18): 217-224

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