Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, domain label, and data structure, are essential to bridging the labeled source domain and unlabeled target domain. However, most existing UDA-based methods use only the former two information and ignore the modeling of data structure, which make the information contained in the features extracted by the deep network incomplete. To tackle this issue, a domain adversarial graph convolutional network (DAGCN) is proposed to model the three types of information in a unified deep network and achieving UDA. The first two types of information are modeled by the classifier and the domain discriminator, respectively. In data structure modeling, a convolutional neural network (CNN) is first employed to exact features from input signals. After that, the CNN features are input to the proposed graph generation layer to construct instance graphs by mining the relationship of structural characteristics of samples. Then, the instance graphs are modeled by a graph convolutional network, and the maximum mean discrepancy metric is leveraged to estimate the structure discrepancy of instance graphs from different domains. Experimental results conducted on two case studies demonstrate that the proposed DAGCN can not only obtain the best performance among the comparison methods, but also can extract transferable features for domain adaptation. The code library is available at: https://github.com/HazeDT/DAGCN.

中文翻译：

---

用于变工况下故障诊断的域对抗图卷积网络


基于无监督域适应（UDA）的方法在可变工况下的机械故障诊断方面取得了巨大进展。在 UDA 中，三类信息（包括类标签、域标签和数据结构）对于桥接标记的源域和未标记的目标域至关重要。然而，现有的大多数基于UDA的方法仅使用前两种信息，而忽略了数据结构的建模，这使得深层网络提取的特征中包含的信息不完整。为了解决这个问题，提出了域对抗图卷积网络（DAGCN）来在统一的深度网络中对三种类型的信息进行建模并实现 UDA。前两类信息分别由分类器和域鉴别器建模。在数据结构建模中，首先采用卷积神经网络 (CNN) 从输入信号中精确提取特征。之后，将CNN特征输入到所提出的图生成层，通过挖掘样本结构特征的关系来构造实例图。然后，通过图卷积网络对实例图进行建模，并利用最大平均差异度量来估计来自不同域的实例图的结构差异。两个案例研究的实验结果表明，所提出的 DAGCN 不仅可以在比较方法中获得最佳性能，而且可以提取可转移特征以进行域适应。代码库位于：https://github.com/HazeDT/DAGCN。