A New Probability Guided Domain Adversarial Network for Bearing Fault Diagnosis

Abstract

Recently, unsupervised domain adaptation (UDA) is widely used in fault diagnosis. Many UDA methods reduce the domain differences through alignment tasks, which allows cross-domain diagnosis of the target domain. However, the alignment and classification tasks are independent in these methods. The alignment task does not actively serve the classification task. Accordingly, the semantic features are polluted in the alignment task, and the prediction certainty of the target domain samples is insufficient. In this study, a new probability guided domain adversarial network (PG-DAN) is proposed. A new domain alignment loss is designed. Specifically, classification ability is used as a measure of domain differences to avoid the contamination of semantic features by alignment tasks. In particular, a classifier is used as a discriminator and the classifier prediction information for feature alignment. Although the prediction with high-confidence probability can be obtained through the abovementioned module, misclassification is still possible. To avoid the misclassification problem, a gradient supervision module is designed to guide alignment tasks. This module explicitly reduces the gradient difference caused by misclassification. Comprehensive experiments show that our method can achieve better diagnostic performance.