Abstract Cross-domain fault diagnosis has garnered significant attention in rolling bearing health monitoring. However, most existing studies focus on fault identification under constant operating conditions and thus struggle to address the data distribution discrepancies caused by variable speed and load in practice. Thus, this research introduces a class-domain alignment with domain importance perception method (CDADIPM) for fault diagnosis across varying operating conditions.CDADIPM integrates conditional maximum mean discrepancy (CMMD) with adversarial learning to extract both shared and class-specific features across domains, enhancing cross-domain feature representation. Additionally, a domain importance perception mechanism is introduced to dynamically assess and assign transfer weights to multiple source domains based on distribution differences within each category, ensuring more accurate and efficient knowledge transfer. To further prevent the exclusion of valuable source domain knowledge due to large distribution differences, a weight constraint mechanism is designed to ensure the effective participation of all source domains. Experimental results demonstrated that CDADIPM achieved accuracies exceeding 97% across all tasks, surpassing the performance of the latest multi-source domain adaptation methods. These results highlight its superior engineering applicability and broader potential for practical deployment.
Liu et al. (Thu,) studied this question.