Fault diagnosis of the aircraft wiring network plays an important role in the intelligent manufacture of the aircraft. Many studies focus on the feature-based machine learning methods. However, these methods are improper in handling the data on heterogeneous graphs. Due to the scatter of the valid feature information, the relevant information between the test nodes is ignored by these methods, which leads to the low accuracy fault diagnosis. Taking the advantage of the 5G technology that can remotely process large-scale graph data, this work proposes a fault diagnosis method named “topological network-graph transformer network (TN-GTN).” TN-GTN can improve the fault diagnosis accuracy through feature enhancement and classification, which is based on the topological information of heterogeneous graphs. The graph network is able to learn new graph structures by identifying useful meta-paths and multi-hop connections between unconnected nodes on original graphs. Feature-enhanced test nodes are used to classify the final labels by the artificial neural network. Results of the performed experiment showed that TN-GTN reduced the dependence on domain knowledge and achieved an accurate classification of the fault diagnosis on aircraft wiring network.

飞机布线网络故障诊断在飞机智能制造中发挥着重要作用。许多研究集中在基于特征的机器学习方法上。然而，这些方法不适用于处理异构图上的数据。由于有效特征信息的分散，这些方法忽略了测试节点之间的相关信息，导致故障诊断准确率低。本工作利用5G技术可以远程处理大规模图数据的优势，提出了一种名为“拓扑网络-图变压器网络（TN-GTN）”的故障诊断方法。TN-GTN可以通过基于异构图拓扑信息的特征增强和分类来提高故障诊断准确率。图网络能够通过识别原始图上未连接节点之间的有用元路径和多跳连接来学习新的图结构。特征增强的测试节点用于通过人工神经网络对最终标签进行分类。实验结果表明，TN-GTN降低了对领域知识的依赖，实现了飞机布线网络故障诊断的准确分类。