Feature Adaptive Modulation and Prototype Learning for Domain Generalization Intelligent Fault Diagnosis

Most existing domain generalization fault diagnosis methods concentrate on learning domain-invariant features or global feature distribution alignment. Nevertheless, this could lose vital clues related to the fault categories, and make adapting to unknown working conditions challenging. To this end, a novel approach termed feature adaptive modulation and health state prototype consistency learning (FAMPL) is proposed. Specifically, FAMPL incorporates a feature adaptive modulation module designed to generate modulation parameters, which are utilized to perform affine transformations on the acquired features, yielding modulation features. This approach aims to capture essential clues associated with specific working conditions. To further enhance the ability to distinguish between different fault categories, a specialized health state prototype learning strategy has been developed. This approach significantly refines the model's capacity for feature discrimination, making it more adept at accurately identifying and categorizing various fault types. Numerous cross-domain fault diagnosis experiments have demonstrated the superiority of FAMPL.