A Universal Domain Adaptation Method With Cluster Matching for Machinery Fault Diagnosis

Fault diagnosis is crucial in the Industrial Internet of Things (IIoT), but unknown fault types lead to out-of-distribution (OOD) problems, making label prediction challenging. Various domain adaptation (DA) methods often rely heavily on prior knowledge of the target domain. This paper proposes a universal domain adaptation (UDA) fault diagnosis method capable of effectively handling various DA settings. The method proposed enables diagnosis without considering the label in the target domain. This eliminates the need to switch between different diagnostic models, greatly enhancing the generalization capability of crossing various tasks and reducing both time and operational costs in engineering applications. The method utilizes clustering algorithms to leverage the changes in data density information, and then address the long-tailed imbalanced data problem to some extent. Through cycle-matching at the category and sample levels, potential unknown categories in the target domain are identified, and samples of shared classes are aligned using contrastive domain discrepancy loss. To mitigate misclassifications during the clustering, extreme-value theory (EVT) models are constructed using source domain samples to filter out incorrect samples. The proposed method is evaluated by constructing experiments with imbalanced data and cross-domain experiments under different working conditions on the WT-planetary gearbox dataset and Twin spool engine (TSE) datasets. Simultaneously, we visualize the analysis of the results. The experimental results demonstrate that the proposed approach can accurately identify unknown fault samples and make improvements in both accuracy and H-score.