An accurate lightweight algorithm for bearings fault diagnosis based on DPW ATTCNN model - Li, Yongfei (Physical communication, 2024)
Rapid and high-precision fault diagnosis of bearings is an essential safeguard for monitoring the safe operation of machinery. Numerous scholars have proposed bearing fault diagnosis models that achieve excellent identification accuracy. However, owing to the elevated cost, some of fault diagnosis models are too complicated to be deployed in practice. This paper proposes a lightweight model based on Depthwise separable convolution (DPW) and Efficient Channel Attention (ECA) called DPW ATTCNN model. The DPW ATTCNN model first adopts DPW to reduce the parameters of the model. Meanwhile, the ECA attention mechanism is utilized to learn crucial information between neural network channels, which improves the accuracy of bearing fault diagnosis. The DPW ATTCNN model is improved based on the Adaptive Batch Normalization (AdaBN) algorithm, which has excellent domain adaptive capability. DPW ATTCNN model achieves excellent performance using a small number of parameters. The experimental results show that the DPW ATTCNN model can achieve 99.58 % accuracy in bearing fault diagnosis, which is verified on the public dataset Case Western Reserve University (CWRU) dataset. In addition, the anti-interference evaluation experiment shows that the accuracy of DPW ATTCNN model for bearing fault diagnosis is still higher than 95 % even under strong noise interference. In the domain adaptive performance test, the DPW ATTCNN (AdaBN) model achieves an average fault recognition rate of 97.35 %, which further verifies the robustness and effectiveness of the proposed DPW ATTCNN algorithm.