Intelligent fault diagnosis based on sample weighted joint adversarial network

Abstract

In recent years, adversarial-based deep domain adaptation (DDA) has attracted increasing attention in transferable fault diagnosis. However, most of the existing methods mainly focus on the elimination of global distribution discrepancies, and neglect the exploration of category-specific distribution characteristics, resulting in unsatisfactory diagnostic results in complex scenarios. To avoid this drawback, a novel sample weighted joint adversarial network (SWJAN) is proposed in this paper, which exploits the category information to enhance the joint domain adaptability of adversarial learning. Specifically, in SWJAN, the generated feature vector for domain recognition is decomposed into a feature matrix according to the label probability, so that the category distribution of the feature space can be captured and aligned. As a result, not only the traditional domain-wise adaptation, but also the class-wise feature matching can be emphasized simultaneously. Moreover, since it is risky to adapt the distribution on low-confidence data, the weight of each sample is dynamically adjusted according to the classification uncertainty, so as to control the influence of ambiguous data on adversarial learning and suppress negative transfer near the decision boundary. Experimental analysis, including cross-domain fault diagnosis of the gearbox and rolling bearing, verifies the practicability and superiority of SWJAN in engineering applications.