Bearing fault diagnosis by combining a deep residual shrinkage network and bidirectional LSTM. (23rd December 2021)

• Record Type: Journal Article
• Authors: Tong, Yizhi; Wu, Ping; He, Jiajun; Zhang, Xujie; Zhao, Xinlong
• Is Part Of: Measurement science & technology. Volume 33:Number 3(2022)
• Publication Date: 2021-12-23
• Subjects: bearing -- fault diagnosis -- deep learning -- deep residual shrinkage network -- bidirectional long short-term memory; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac37eb
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Bearings are indispensable and key components in rotating machinery. To ensure the safe and reliable operation of rotating machinery, bearing fault diagnosis plays a crucial role. To explore the spatial and temporal information in vibration signals, a novel bearing fault diagnosis method is proposed by combining a deep residual shrinkage network (DRSN) and bidirectional long short-term memory (Bi-LSTM) network in this study. Firstly, a DRSN is employed to extract the spatial features from noise-related vibration signals. Then, a Bi-LSTM network is adopted to further address the long-term dependencies problem in vibration signals, where the temporal information is exploited. By integrating DRSN and Bi-LSTM, the spatial and temporal information of vibration signals is fully extracted. Finally, a fully connected layer with Softmax is used to offer the diagnostic results. Experimental results using two case studies demonstrate the effectiveness of the proposed method by comparison with other state-of-the-art methods.