Specific Task-Guided Collaborative Domain Generalization Network for Intelligent Fault Diagnosis Under Unseen Conditions

Domain generalization-based methods perform cross-domain fault diagnosis by learning fault-discriminative and domain-invariant diagnostic knowledge between available working conditions (source domains) and unseen conditions (target domains). However, existing approaches ignore domain specificity within discriminative knowledge, preventing optimal fault discrimination across diverse domains. Moreover, since the target domain is inaccessible during model training, obtaining sufficient domain-invariant knowledge from the limited source domains poses a significant challenge. For the weaknesses, a specific task-guided collaborative domain generalization network (STCDGN) is proposed to enhance bearing fault diagnosis under unseen working conditions. Specifically, we construct a channel attention-guided multi-scale feature extractor and task-specific classifiers to establish adaptive decision boundaries for domain specificity. The boundaries interact with the extracted features to enhance fault-discriminative representations. To further mine domain invariance within these representations, we propose a two-stage training strategy through decision boundary divergence maximization and multi-scale hierarchical feature discrepancy minimization, effectively alleviating intra-class domain shift for improved diagnostic generalization. Finally, we propose an entropy-guided decision selection strategy for reliable inference diagnostic results. The average accuracies on the two public datasets reach 98.72% and 89.13%, respectively, indicating significant diagnostic generalization to the unseen target samples. The visualized experimental results further demonstrate the method’s effectiveness in learning fault-discriminative and domain-invariant diagnostic knowledge.