Dynamic Confusion-Aware Correlation Network for Cross-Domain Fault Diagnosis

In recent years, domain adaptation has emerged as a promising method for overcoming limitations in labeled data availability and shifting data distributions in fault diagnosis. However, current unsupervised domain adaptation methods primarily emphasize global alignment between source and target domain distributions, ignoring the impact of hard-to-transfer classes and individual samples on transfer performance. To address this, a dynamic confusion-aware correlation network (DCACN) is proposed to achieve significant transfer gains by focusing on aspects with high confusion of the transfer process. For class-level confusion, class confusion is incorporated into the target domain class correlation matrix through a reweighting mechanism based on the source domain confusion matrix. The minimum class confusion (MCC) theory is leveraged to mitigate inter-class confusion, specifically for high-confusion classes. For sample-level confusion, a dynamic Focal Loss is proposed by integrating a transfer confusion factor derived from the confusion-aware correlation matrix, enhancing the ability to focus on highly confusing samples. Experimental evaluations on public and real-world datasets demonstrate the effectiveness of the proposed method, showing significantly improved accuracy in cross-domain fault classification compared to multiple state-of-the-art methods.