Adaptive centroid prototype-based domain adaptation for fault diagnosis of rotating machinery without source data

Domain adaptation can effectively achieve fault diagnosis tasks with unlabeled target data using similar labeled source datasets during the training stage. However, the labeled source datasets are usually not directly accessible due to data privacy concerns, which restrict the application of the domain adaptation-based fault diagnosis methods. In this study, an adaptive centroid prototype-based domain adaptation (ACPDA) method is proposed to conduct fault diagnosis tasks in the unlabeled target domain without accessing source data. In ACPDA, an entropy-based adaptive prototype memory matrix is constructed to filter reliable samples and define the initial pseudo-label in the target domain. The centroid prototype is designed using all target data to update the pseudolabel and avoid confidence bias. Furthermore, the information maximization loss function is employed to reduce the feature distribution discrepancies. Extensive experiments on real wind turbine gearbox datasets demonstrate the effectiveness and superiority of the proposed ACPDA method.