The Open Set Weighted Domain Adaptation Method for Fault Diagnosis of Rolling Bearings

The transfer fault diagnosis method can identify the fault of rolling bearing dataset collected under different working conditions or different equipment, which realized the cross-dataset application of fault knowledge. However, the existing transfer fault diagnosis methods all assume that the labeled training dataset and unlabeled testing dataset have the same fault label set. The above assumption is not realistic. Because the fault label in the unlabeled testing set is unknown, which makes it difficult to determine whether this fault label also belong to the label set of the training dataset. This paper considers an open set fault diagnosis problem, where the label set of the training dataset is a subset of the label set of testing dataset. In order to solve this problem, a novel open set weighted domain adaptation method is proposed for fault diagnosis of rolling bearings. The weighted domain discriminator is mainly designed in propose method by combining domain similarity and prediction uncertainty, which solves the problem of traditional transfer fault diagnosis cannot complete the domain alignment of open set data. Finally, the open set testing module is designed to distinguish between unknown label data and known label data by setting a threshold. The proposed method is validated by the rolling bearing datasets collected from failure test bench of the gearbox bearing. The results show that the proposed method can realize fault diagnosis for open set of rolling bearing dataset.