Working Condition-Decoupled and Invariant-Feature Fusion Transformer for Domain Generalization Intelligent Fault Diagnosis

For fault diagnosis under unseen working conditions (WCs), it is crucial to extract general knowledge unrelated to data distribution from available source data and identify transferable discriminative features. However, WC-related information is often tightly coupled with health state (HS)-related information, making it difficult to directly distinguish their contributions, posing challenges to fault diagnosis. To address this issue, a novel approach named WC-decoupled and invariant-feature fusion transformer (WCD-IFFT) is proposed, which aims to minimize the impact of WCs by extracting transferable features closely related to HSs. Specifically, two key components are designed to decouple WC-related features from HS-related features: orthogonality separation and decouple loss. Additionally, to enrich the semantics of HS-related features, time-domain and Fourier phase features are mapped into a unified space and fused, combining the instantaneous changes of time-domain signals with frequency-domain distribution information to enhance the feature representation capability. Extensive experiments on cross-domain fault diagnosis tasks demonstrate the effectiveness of the proposed method.