Knowledge-driven domain adaptation strategy for rotating machinery fault diagnosis under varying working condition

Abstract

Due to the frequent switch of the working condition, fault diagnosis model for rotating machinery established on the training set (the source domain) cannot be effectively applied to the test set (the target domain). Traditional domain adaptation methods address this issue by performing feature alignment between the source and target domain, which ignores the positive guidance provided by prior knowledge. In this paper, we propose a prior knowledge-driven domain adaptation (PKDA) method for varying working condition fault diagnosis of rotating machinery, where a self-supervised learning framework is designed to integrate expert prior knowledge and structural prior knowledge. First, an expert prior knowledge guidance module is designed to extract features with physical significance. Then we align the features between the source and target domain by the max mean discrepancy metric. Besides, a progressive Shannon entropy minimization strategy is proposed to realize the feature distribution structure of intra-class compression and inter-class separation, which can effectively integrate the structural prior knowledge. In this way, PKDA can effectively utilize the prior knowledge to achieve better performance in varying working condition fault diagnosis tasks. The effectiveness of the proposed method is illustrated by an open-source rolling bearing fault dataset from Case Western Reserve University and an open-source gearbox fault dataset from Southeast University.