Self-Supervised Pseudo-Label Learning-Enabled Cross-Domain Fault Diagnosis Method Under Time-Varying Speeds

Most industrial equipment works under variable conditions, variable conditions would increase the within-class difference and reduce the cross-class difference of fault samples. The existing methods mainly consider steady speed scenarios and the global domain adaptation while ignoring the within-class and cross-class distribution alignment, the fault distribution variation leads to the deterioration of diagnosis performance. In this article, a self-supervised pseudo-label learning-enabled (SPL) cross-domain diagnosis method is proposed for fault diagnosis under time-varying speeds. Specifically, the Cauthy maximum mean-square discrepancy is designed for global distribution-level feature alignment by reducing the domain discrepancy. The pseudo-label training and consistency regularization are established for decision boundary adjustment by optimizing the probability distribution difference between the target domain and its perturbed output. Besides, uncertainty-reweighted class confusion minimization is introduced in within-class and cross-class distribution alignment to decrease negative transfer caused by huge within-class discrepancies and small cross-class differences, which can effectively improve the diagnosis accuracy of the hard-to-identify confusion samples. Experiments on time-varying fault diagnosis tasks show the superior performance of the proposed method. The proposed SPL framework improves average diagnosis accuracies by at least 8%.