A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism

Fault diagnosis of rolling bearings helps ensure mechanical systems' safety. The characteristics of temporal and interleaved noise in the bearing fault diagnosis data collected in the industrial field are addressed. This paper proposes a hybrid intelligent fault diagnosis method (WKN-BiLSTM-AM) based on WaveletKernelNetwork (WKN) and bidirectional long-short term memory (BiLSTM) network with attention mechanism (AM). The WKN model is introduced to extract the relevant impact components of defects in the vibration signals, reduce the model training parameters and facilitate the processing of signals containing noise. Then, the fusion of spatial-temporal features is achieved by combining BiLSTM networks to compensate for the lack of individual networks that ignore the dependent information between discontinuous sequences. Finally, the AM module is introduced to improve the feature coding performance of BiLSTM and fault diagnosis accuracy. Comparison and validation between the proposed WKN-BiLSTM-AM method and other state-of-the-art models are given on the Case Western Reserve University and Paderborn University datasets. The experimental results verify the effectiveness of the proposed model in bearing fault diagnosis, and the model's generalization capability.