Bearing fault diagnosis by combining a deep residual shrinkage network and bidirectional LSTM

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-LS TM 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-LS TM, 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.