A Bidirectional Structure Constraint framework for Domain Generalization in Intelligent Fault Diagnosis
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Abstract
Achieving robust generalization in intelligent fault diagnosis under diverse industrial conditions remains challenging. Most domain generalization (DG) methods focus on either feature compactness or category separation, seldom addressing both in a unified framework. To overcome this, we propose a Bidirectional Structure Constraint (BSC) framework comprising Momentum Feature Alignment (MFA) and Category Anchor Separation (CAS). MFA employs a momentum-driven strategy to capture domain-invariant features for each category, while CAS encourages learnable class anchors to repel each other in latent space, enhancing class separability. These objectives are jointly optimized in a multi-loss framework, enabling the model to learn representations that are both intra-class compact and inter-class distinct. Experiments on the Shandong University of Science and Technology (SDUST) rotating machinery fault diagnosis dataset show that BSC significantly improves cross-domain generalization.
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Domain Generalization, Fault Diagnosis, Feature Alignment
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