面 向 不 同 标 签 与 域 配 置 的 统 一 跨 域 故 障 诊 断 方 法

Reliable fault diagnosis is crucial to ensuring the safe and efficient operation of high-end industrial equipment. Cross-domain intelligent diagnosis technologies based on unsupervised domain adaptation (UDA) have demonstrated promising application prospects in scenarios such as cross-equipment and variable working transfer diagnosis conditions. However， their effectiveness highly relies on specific prior assumptions regarding the inter-domain label relationships and domain configurations， which largely restricts the generalizability and practicality of UDA techniques in actual industrial fault diagnosis scenarios. To address the above issues，this paper proposes a unified cross-domain fault diagnosis framework applicable to different label and domain configurations. The proposed framework constructs a predictive class confusion (PCC) bias shared across multiple scenarios to guide cross-domain knowledge transfer， enabling adaptation to various transfer diagnostic scenarios. To accurately measure the tendency of the PCC bias， a prototype similarity-based fault discrimination method is developed， which enhances classification robustness and provides reliable prediction distributions to estimate the PCC bias. Then， a label smoothing-based probability calibration method is designed for probability regularization， alleviating the underestimation of the PCC bias caused by overconfident prediction. Experimental validation results on a planetary gearbox transmission system dataset demonstrate that the proposed method achieves an average diagnostic accuracy of 98.37% across four cross-domain diagnostic scenarios with different label and domain configurations，outperforming state-of-the-art approaches and fully verifying its generality and superiority.