【摘要】 可靠的设备健康监测与故障诊断技术是保证高端装备安全高效运行的关键。基于无监督域自适应的跨域智能诊断技术已在跨设备、变工况等迁移诊断场景中展现出广阔的应用前景。然而，此类方法依赖域间标签关系和域配置的特定事前假设，致使无监督域自适应技术在实际工业故障诊断场景中的泛化性与实用性受限。针对上述问题，本文提出一种面向不同标签与域配置的统一跨域故障诊断方法。该方法构建一种多场景共享的预测类别混淆偏差用于指导跨域知识迁移，从而适应各种跨域故障诊断场景。为更准确地度量预测类别混淆偏差，提出一种基于原型相似度的故障判别方法以增强分类鲁棒性，从而为估计预测类别混淆偏差提供可靠的预测分布。此外，设计了一种基于标签平滑的概率校准方法进行概率正则化，以缓解过度自信预测导致的预测类别混淆偏差低估。行星齿轮箱传动系统数据集试验验证结果显示，所提方法在4种不同标签和域配置的跨域诊断场景中，平均诊断准确率达到98.37%，相较于前沿对比方法具有优势，充分验证了所提方法的通用性和优越性。更多还原

【Abstract】 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 crossdomain 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.更多还原