A physics-guided diffusion-GAN network with VMD feature transfer for imbalanced fault diagnosis

Abstract

Sample imbalance across operating conditions severely constrains data-driven bearing fault detection performance. This challenge is more acute due to the complete absence of fault samples under target operating conditions. To address this limitation, this paper proposes a framework integrating variational mode decomposition (VMD), nonlinear dynamic modeling, and physics guidance. First, a two-degree-of-freedom nonlinear system is established to simulate physically realistic dynamic behaviors. Second, VMD decouples fault feature components from source-domain fault signals. These components are superimposed with target-domain normal signals and dynamic responses to form hybrid samples. Then, a diffusion-based wasserstein generative adversarial network architecture is adopted. Simultaneously, auxiliary classifiers for operating conditions and fault types are incorporated as physics-regularized losses to enforce physical consistency. The fault feature decoupling component based on VMD and the generation framework jointly mitigate the domain gap between dynamically generated response signals and measured signals. Experimental results indicate generated samples from the proposed method achieve superior scores across multiple evaluation metrics. And classification networks trained on generated samples achieve diagnostic accuracy exceeding 90% in multiple cross-condition tasks, which confirms the superiority of the developed model.