A Multi-source Domain Generalization Network for Rotating Machinery Fault Diagnosis under Unseen Operating Conditions

With the rapid development of industrial intelligence, the domain adaptation technology depending on the availability of the target domain has gradually become a reliable solution to address the performance degradation of fault diagnosis due to the variation of operating conditions for rotating machinery. However, the fault diagnosis scenarios under unseen operating conditions are commonly confronted in practical engineering. In response to the above limitations, a novel multi-source domain generalization network (MDGN) is proposed in this paper to enhance the fault diagnosis performance of rotating machinery under unseen operating conditions by decoupling the domain-relevant feature information for obtaining domain-invariant diagnosis knowledge. First, a temporal convolutional autoencoder (TCAE) is developed as a feature extraction module to fully capture the effective spatio-temporal information of the sample data. Then, a domain classification module is designed for adversarial transfer learning to facilitate the acquisition of domain-invariant features. Finally, domain divergence loss and boundary margin loss are constructed for MDGN to supervise the feature distribution alignment among domains and feature distinction among classes. The mechanical comprehensive diagnosis simulation platform (MCDSP) bearing dataset collected in our laboratory is employed to evaluate the domain generalization performance of the proposed method. The experimental results demonstrate that the method achieves an average fault diagnosis accuracy of 94.50%, which is better than the comparison algorithms.