Working Condition-Decoupled and Invariant-Feature Fusion Transformer for Domain Generalization Intelligent Fault Diagnosis
提出WCD-IFFT方法，通过解耦工作条件与健康状态特征并融合时域和频域信息，提升跨工况故障诊断的泛化能力。
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.