Dual-branch self-guided network for single-domain generalization in sensor fault diagnosis
Most domain-generalization methods for sensor fault diagnosis require multiple labeled source domains. When only one source domain is available, inter-domain variations cannot be directly observed, making it difficult to identify informative samples and avoid overfitting to source-specific features. To address these challenges, a dual-branch self-guided network (DBSGN) is proposed. In the guidance branch, energy discrepancy and local feature distance identify class-representative samples, while style consistency selects domain-representative samples. In the decision branch, multi-scale feature extractors learn complementary representations, and distribution-uncertainty-guided interleaved learning enables the fault classifier to exploit different feature scales. Two-stage contrastive learning improves cross-scale distribution consistency, intra-class compactness, and inter-class separability. Scale-blurring adversarial training further suppresses scale-specific information and promotes transferable representation learning without target-domain access during model development. Experiments on real-world sensor data from a nickel flash smelting system show that DBSGN achieves an average accuracy of 94.89% across twelve cross-process-domain diagnostic tasks, 3.44 percentage points higher than the best comparison method under the same protocol. These results support its effectiveness in the studied single-source domain-generalization setting.