TN-GTN: fault diagnosis of aircraft wiring network over edge computing

Abstract

AbstractFault 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.