Unsupervised domain adaptation has achieved certain success in recent cross-domain fault diagnosis research. As a widely used transfer strategy, the distribution alignment often occurs with the problems of too few valid alignment samples, too low confidence of predicted labels, and the inadequate alignment of marginal or conditional distributions. Therefore, this paper proposes a statistical distribution recalibration method of soft labels (SDRS). First, SDRS defines the valid samples and confusion interval in the statistical distribution of per-class predicted probabilities. Then, from the perspective of binary classification, a recalibration space in the confusion interval is further optimized by a center distance metric, to improve predicted confidence and valid distribution alignment. Built on SDRS, a novel cross-domain fault diagnosis approach named SDRS-DAN is constructed, where dynamic distribution adaptation is used to match and adjust the marginal and conditional distribution discrepancies adaptively. Extensive experiments prove the effectiveness of SDRS-DAN in cross-location and cross-machine scenarios.

无监督域自适应在最近的跨域故障诊断研究中取得了一定的成功。作为一种广泛使用的迁移策略，分布对齐经常存在有效对齐样本太少、预测标签置信度太低、边缘或条件分布对齐不充分等问题。因此，本文提出了一种软标签统计分布重标定方法（SDRS）。首先，SDRS 定义了每类预测概率的统计分布中的有效样本和混淆区间。然后，从二元分类的角度，通过中心距度量进一步优化混淆区间中的重新校准空间，以提高预测置信度和有效分布对齐。建立在 SDRS 之上，构建了一种名为 SDRS-DAN 的新型跨域故障诊断方法，其中使用动态分布自适应来自适应地匹配和调整边际和条件分布差异。大量实验证明了 SDRS-DAN 在跨位置和跨机器场景中的有效性。