Despite the remarkable results that can be achieved by data-driven intelligent fault diagnosis techniques, they presuppose the same distribution of training and test data as well as sufficient labeled data. Various operating states often exist in practical scenarios, leading to the problem of domain shift that hinders the effectiveness of fault diagnosis. While recent unsupervised domain adaptation methods enable cross-domain fault diagnosis, they struggle to effectively utilize information from multiple source domains and achieve effective diagnosis faults in multiple target domains simultaneously. In this paper, we innovatively proposed a weighted joint maximum mean discrepancy enabled multi-source-multi-target unsupervised domain adaptation (WJMMD-MDA), which realizes domain adaptation under multi-source-multi-target scenarios in the field of fault diagnosis for the first time. The proposed method extracts sufficient information from multiple labeled source domains and achieves domain alignment between source and target domains through an improved weighted distance loss. As a result, domain-invariant and discriminative features between multiple source and target domains are learned with cross-domain fault diagnosis realized. The performance of the proposed method is evaluated in comprehensive comparative experiments on three datasets, and the experimental results demonstrate the superiority of this method.

翻译：尽管数据驱动的智能故障诊断技术可以取得显著成果，但它们的前提条件是训练数据和测试数据具有相同分布，且需要充足的标记数据。实际工况中常存在多种运行状态，导致域偏移问题阻碍故障诊断有效性。现有无监督域适应方法虽能实现跨域故障诊断，但难以有效利用多源域信息并同时实现多目标域的精准诊断。本文创新性地提出一种基于加权联合最大均值差异的多源-多目标无监督域适应方法（WJMMD-MDA），首次实现了故障诊断领域中多源-多目标场景下的域适应。所提方法从多个标记源域中充分提取信息，通过改进的加权距离损失实现源域与目标域之间的域对齐，从而学习到多源域与多目标域之间域不变且具有判别性的特征，实现跨域故障诊断。本文在三个数据集上进行全面对比实验以评估该方法性能，实验结果表明了该方法的优越性。