Recently, substantial research has explored the development of deep-learning-based methods to diagnose faults in rotating machinery. For these diagnosis methods, it is difficult to obtain high target diagnosis accuracy when the amount of labeled data obtained pertaining to the rotating machinery under study is insufficient or in cases involving a discrepancy in the distribution types found in the training and test data sets. To deal with this research need, the paper outlines a new method, a domain adaptation with semantic clustering (DASC), capable of diagnosing faults in rotating machinery. The method outlined in this research learns both domain-invariant and discriminative features. The method reduces the domain discrepancy by minimizing the domain-related loss. In addition, by defining an additional loss, which is called semantic clustering loss, and reducing it at multiple feature levels, the DASC method learns features that make samples better semantically clustered, according to their health conditions. Consequently, fault diagnosis performance for target rotating machinery can be enhanced through the use of the DASC approach. The effectiveness of the DASC approach is confirmed by examining various fault diagnosis situations with domain discrepancies across the source and target domains, using experimental data from three bearing systems. Also, various analyses are explored to better understand the advantages of the DASC method.

最近，大量研究探索了基于深度学习的方法来诊断旋转机械故障的发展。对于这些诊断方法，当获得的与研究中的旋转机械有关的标记数据量不足时，或者在训练和测试数据集中发现的分布类型存在差异的情况下，很难获得高目标诊断精度。为了应对这一研究需求，本文概述了一种新方法，即带有语义聚类的域自适应 (DASC)，能够诊断旋转机械中的故障。本研究中概述的方法学习了域不变和判别特征。该方法通过最小化域相关损失来减少域差异。此外，通过定义一个额外的损失，这被称为语义聚类损失，并在多个特征级别上减少它，DASC 方法根据其健康状况学习使样本在语义上更好地聚类的特征。因此，通过使用 DASC 方法可以提高目标旋转机械的故障诊断性能。DASC 方法的有效性通过使用来自三个轴承系统的实验数据检查具有跨源域和目标域的域差异的各种故障诊断情况来确认。此外，还探索了各种分析以更好地了解 DASC 方法的优点。通过使用 DASC 方法可以提高目标旋转机械的故障诊断性能。DASC 方法的有效性通过使用来自三个轴承系统的实验数据检查具有跨源域和目标域的域差异的各种故障诊断情况来确认。此外，还探索了各种分析以更好地了解 DASC 方法的优点。通过使用 DASC 方法可以提高目标旋转机械的故障诊断性能。DASC 方法的有效性通过使用来自三个轴承系统的实验数据检查具有跨源域和目标域的域差异的各种故障诊断情况来确认。此外，还探索了各种分析以更好地了解 DASC 方法的优点。