Bearing fault diagnosis domain generalization network based on multi-scale feature alignment

Abstract Domain generalization in fault diagnosis (FD) faces significant challenges, primarily due to its inability to fully leverage multi-scale feature information. This study proposes the multi-scale feature alignment-based domain generalization network for bearing FD, which overcomes the limited diagnostic performance of previous networks that focus solely on single-scale feature information. First, the generalized s-transform is employed to convert one-dimensional vibration signals into two-dimensional time-frequency representations. Next, a multi-scale feature extractor is designed by integrating the coordinate attention mechanism, the gate recurrent unit module, and the transformer encoder module, enhancing the model’s capacity to extract features at multiple scales. Furthermore, the training process is enhanced by combining the maximum mean discrepancy and correlation alignment (Coral) loss functions. Finally, adversarial training strategies are employed to boost the diagnostic accuracy of the discriminator, thereby strengthening the model’s adversarial diagnostic capabilities. The proposed method was experimentally validated using a self-constructed bearing fault dataset and a publicly available bearing fault dataset from Jiangnan University (JNU). The results indicate that, on the self-constructed dataset, the proposed method achieves an accuracy of 98.54% in the multi-source domain diagnosis task, marking a 6.74% improvement over the inadequate source domain task. Additionally, for the insufficient source domain diagnosis task on the JNU dataset, an average accuracy of 92.52% was attained. These results thoroughly demonstrate the reliability and effectiveness of the proposed method.