Open-Set Cross-Domain Diagnosis via Prototype Contrastive Learning and Adversarial Entropy Decoupling

New fault types may continually emerge under varying operating conditions, while conventional domain adaptation methods often fail to recognize them as unknown. To address this issue, this study proposes PC-AED, an open-set cross-domain fault diagnosis method driven by prototype-guided contrastive learning and adversarial entropy decoupling. The method uses multi-view augmentation in the time, frequency, and time–frequency domains to improve the stability of vibration features under operating-condition changes and noise. Dynamic class prototypes are used to constrain the feature distributions of the source and target domains. This promotes the aggregation of same-class features and the separation of different-class features. Source-domain predictive entropy is used to determine a dynamic uncertainty threshold. Adversarial learning then refines the decision boundary between known and unknown classes. Nineteen open-set cross-domain diagnosis tasks were constructed using the CWRU and SEU bearing datasets. PC-AED achieved mean H-scores of 98.04% and 93.83% on the CWRU and SEU datasets, respectively. These values exceeded those of the best-performing baseline method by 6.22 and 4.48 percentage points, respectively. The results show that PC-AED maintains its ability to recognize known faults under varying operating conditions while effectively detecting unknown faults. It provides a feasible approach to rotating machinery fault diagnosis in open industrial environments.