A Novel Class Prototype-Guided Open-Set Domain Adaptation Method for Mechanical Fault Diagnosis

Open-set domain adaptation (OSDA) can address the realistic fault diagnosis (FD) scenario where the target domain contains previously unseen fault classes. However, most of the previous studies usually adopt coarse-grained separation approaches and ignore the important role of the closed-set model itself, which affects the FD performance. To address this limitation, a novel class prototype-guided OSDA (CPOSDA) method for mechanical FD (MFD) is proposed. First, a multisource adversarial pretraining approach is designed, which provides a high-quality closed-set model for constructing reliable cross-domain class prototypes. Second, a class prototype-guided progressive separation strategy is developed, which leverages the class prototypes for coarse-grained separation of target domain samples, followed by fine-grained training to refine the decision boundary between known and unknown classes. Third, a domain adaptation (DA) strategy with unknown-class rejection is designed to filter out samples from unknown classes during the distribution alignment process between the source and target domains, thereby reducing the risk of negative transfer. Finally, extensive experiments prove that the proposed method achieves excellent cross-domain FD performance in the open-set scenario and outperforms the comparison methods. The source code is available at https://github.com/NullIsFine/CPOSDA