Gradient consistency strategy cooperative meta-feature learning for mixed domain generalized machine fault diagnosis

Recently, fault diagnosis methods based on domain generalization (DG) have been developed to improve the diagnostic performance of unseen target domains by multi-source domain knowledge transfer. However, existing methods assume that the source domains are discrete and that domain labels are known a priori, which is difficult to satisfy in complex and changing industrial systems. In addition, the gradient update conflict caused by the specific information of source domains leads to the degradation of the DG performance. Therefore, in this study, we relax the discrete domain assumption to the mixed domain setting and propose a novel gradient-consistency strategy cooperative meta-feature learning for mixed-domain generalized machine fault diagnosis. First, a domain feature-guided adaptive normalization module is proposed to normalize the underlying distribution of multi-source domains, and the mixed-source domains are divided into potential domain clusters. Then, a novel meta-feature encoding method is proposed to explicitly encode the overall fault feature structure, which is used to learn the generalized fault feature representation. Finally, a novel gradient consistency update strategy is designed to reduce the impact of domain-specific differences on model generalization. The effectiveness and superiority of the proposed method are verified on many DG diagnostic tasks on two public bearing datasets and the nuclear circulating water pump planetary gearbox dataset.