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.

本研究提出了一种多源多目标无监督领域适应方法，通过改进的加权距离损失函数，在故障诊断领域中实现了源域与目标域之间的域对齐，并学习到多个源域与目标域之间的域不变和有区分性的特征，从而实现了跨域故障诊断。综合对三个数据集进行了综合比较实验，实验结果证明了该方法的优越性。