Minimizing predictive class confusion: A unified framework for cross-domain fault diagnosis under different label and domain configurations

Reliable fault diagnosis is essential for maintaining the safety and operational efficiency of advanced industrial equipment. Diagnostic methods based on transfer learning techniques such as unsupervised domain adaptation have demonstrated considerable potential for engineering applications. However, existing methods rely on the predefined specific assumptions regarding inter-domain label relationships and domain configurations, which severely restrict their practical applications. To address these issues, this study proposes a unified cross-domain fault diagnosis framework for transfer diagnostic tasks under different label and domain configurations, including closed-set, partial-set, open-set, multi-source domain, and multi-target domain transfer diagnostics. The presented unified framework leverages a predictive class confusion bias shared across multiple scenarios to guide cross-domain knowledge transfer, thus enabling effective domain adaptation to various transfer diagnostic scenarios. To measure the tendency of class confusion accurately, a prototype similarity-based fault discrimination method is developed, which enhances classification robustness and provides reliable prediction distributions for predictive class confusion estimation. Then, a label smoothing-based probability calibration mechanism is designed for probability regularization, mitigating erroneous class confusion estimation caused by prediction bias. Additionally, an open-set cross-domain diagnosis method with an adaptive threshold is provided to handle potential unseen faults, which has a straightforward design and can be implemented easily within the unified cross-domain diagnosis framework. Extensive experiments on two transmission system datasets verify the general applicability of the proposed unified framework across five cross-domain diagnosis settings, and its performance is competitive with advanced scenario-specific transfer diagnosis methods, providing an effective tool for intelligent diagnosis in industrial scenarios.