Research on Fault Diagnosis of Rolling Bearing Based on SEMSCNN and GRU Model. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Youfu, Tang; Teng, Wang; Tao, Wang; Na, Lei
• Is Part Of: Journal of physics. Volume 2184:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2184/1/012054
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper, a deep neural network model fault diagnosis method based on the fusion of SENet-MSCNN and GRU is proposed to address the problem of low diagnosis rate caused by the fact that the normal samples are much larger than the fault samples in the collected rolling bearing vibration big data. The method takes the time-domain vibration signal as input and fuses the spatial features extracted by SE-MSCNN and the temporal features extracted by GRU to bring into the fully connected layer for identification, to realize the intelligent diagnosis of rolling bearing adaptive feature extraction. Finally, the method is applied to the simulated signal and experimental data for testing and analysis respectively. The results show that the model can reach 100% fault recognition rate under variable speed conditions, and at the same time has strong noise immunity, adaptivity and robustness. Thus, providing an effective new way for intelligent diagnosis of rolling bearing vibration big data.