Bearing Fault Diagnosis of Split Attention Network Based on Deep Subdomain Adaptation

Abstract

The insufficient learning ability of traditional convolutional neural network for key fault features, as well as the characteristic distribution of vibration data of rolling bearing collected under variable working conditions is inconsistent, and decreases the bearing fault diagnosis accuracy. To address the problem, a deep subdomain adaptation split attention network (SPDSAN) is proposed for intelligent fault diagnosis of bearings. Firstly, the time-frequency diagram of a vibration signal is obtained by the continuous wavelet transform to show the time-frequency characteristics. Secondly, a residual split-attention network (ResNeSt) that integrates multi-path and channel attention mechanisms is constructed to extract the key features of rolling bearings to prevent feature loss. Then, a subdomain adaptation layer is added to ResNeSt to align the distribution of related subdomain data by minimizing the local maximum mean difference. Finally, the SPDSAN model is validated using the Case Western Reserve University datasets. The results show that the average diagnostic accuracy of the proposed method is 99.9% when the test set samples are not labeled, which is higher compared to the accuracy of other mainstream intelligent fault diagnosis models.