Domain generalization for machine compound fault diagnosis by Domain-Relevant Joint Distribution Alignment

In machine fault diagnosis, domain generalization methods have gained significant attention due to their advantage in non-requirement of priori target domain distribution. However, they pose great challenges in domain-relevant feature learning and incurs inferior unseen-domain classification when large domain divergence exists. To address this issues, we propose a novel distribution alignment strategy named DRJDA (Domain-Relevant Joint Distribution Alignment) that matches the domain-joint distribution and domain- relevant distribution for domain generalization fault diagnosis. Specifically, the alpha-PE divergence is employed to minimize the distribution discrepancy, which is demonstrated to be explicitly derived as the maximum value of a quadratic function. Additionally, a parameter-free plug-and-play data augmentation module that performs feature-level instance mixture and style transfer to increase the generalization ability. Finally, data from the China Light-duty Vehicle Test Cycle (CLTC) tests are used as case studies, and the experiments carried out across 14 different domains prove the proficiency of the proposed DRJDA in learning domain-invariant feature when significant domain divergence exists, indicating its remarkable potential in compound fault diagnosis for industrial machinery.