Subdomain Uncertainty Optimization for Cross-Speed Fault Diagnosis

Abstract

Cross-speed bearing fault diagnosis based on unsupervised domain adaptation can handle data distribution differences across various operating speeds, supporting intelligent maintenance of equipment like wind turbines with variable operating speeds. Existing methods focus on aligning sample distributions between source and target domains through global or subdomain correlations. However, these methods overlook essential relationships, such as possible high sample similarity between target subdomains and discrepancies in decision boundaries between source and target domains, leading to sub-stantial class confusion issues. To address class confusion, this paper proposes a subdomain uncertainty optimization method by using these relationships. Class uncertainty is proposed to quantify the degree of classification ambiguity among target domain samples, facilitating the differentiation of high-similarity samples. Boundary optimization is introduced to refine decision boundaries learned from the source domain, alleviating the adverse effects of boundary discrepancies between domains. Additionally, the CL-CNN network is adopted and adjusted to collaborate with the class uncertainty term and boundary optimization term, thus achieving optimal cross-speed fault diagnosis. Extensive experiments conducted across 18 cross-speed tasks demonstrate the superiority of the proposed method, which achieves a stable average accuracy of 99.86%. All code will be released on https://github.com/IWantBe/SUO.