Learnable Center Domain Generalization Module for Rotating Machinery Fault Diagnosis Under Cross-Working Conditions
Domain generalization (DG) has been widely applied to rotating machinery fault diagnosis. Under extreme working conditions and coupled influencing factors, existing DG methods are prone to issues such as batch oscillations and early gradient optimization conflicts. To address these issues, this article proposes a novel method named the gradient-driven learnable class-center network (GLC-Net). GLC-Net incorporates two core mechanisms: 1) a globally learnable center for momentum updates. GLC-Net replaces passive batch-wise statistical updates with gradient-driven updates to the center parameters, and combines this with a momentum mechanism to alleviate optimization oscillations and 2) dynamic curriculum learning strategy is introduced. This strategy effectively prevents negative transfer by balancing the learning of discriminative features and domain-invariant features. Experimental results on both bearing and gearbox datasets demonstrate that GLC-Net achieves superior generalization performance and robustness.