Self-attention and subdomain adaptive adversarial network for bearing fault diagnosis under varying operation conditions

Domain adaptive technology is widely used in the bearing fault diagnosis of variable operating conditions. However, most domain adaptive technology only focuses on the global domain distribution and ignores the subdomain distribution, and the domain-invariant feature quality is easily affected by noise, leading to a significant decrease in diagnostic accuracy under varying operation conditions. Therefore, a fault diagnosis method based on a self-attention subdomain adaptive adversarial network (SASAAN) is proposed.

First, a convolutional block attention module (CBAM) is utilized to extract the fault-related domain-invariant features in the vibration signals of the source and target domains. The adversarial network and subdomain adaptive module are then combined to reduce differences in the global and local domain edge distributions of different operating condition data, thereby improving the transferability of the data. The loss function is optimized by back propagation using the Adam optimizer to improve the diagnostic performance of the model, and the hyperparameter tuning of the model is also performed. Finally, the diagnostic results on the target domain test set are output by the failure classifier, and the Ottawa bearing data set is used to validate the effectiveness of the proposed method. ,

The results show that the fault diagnosis accuracy of the proposed method is higher than 96% under the condition of strong noise and varying operation conditions, which is obviously better than other methods.

The results of this study can provide valuable references for the fault diagnosis of rolling bearings under varying operation conditions.