Fault diagnosis of helicopter tail-drive system using a multi-grained hierarchical message graph convolutional networks

The fault diagnosis of the tail-drive of helicopter is a crucial task for helicopter system operation and maintenance. Recently, graph convolution network (GCN) has been the focus in fault diagnosis for its powerful representational ability in relationship mining. However, with the difficulty of obtaining node and edge information in the high-order domain, the stable performance of the long-range message-passing process of the deep GCN is unknown limits the application of GCN in fault diagnosis. To address these issues, a multi-grained hierarchical message graph convolutional network (MHGCN) is proposed to diagnose faults of helicopter tail-drive system. First, time-frequency characteristics of the original vibration signals are extracted to construct the graph nodes. The original graph nodes are aggregated by Louvain community detection, which can effectively learn the multi-grained features. Then, the hierarchical graph is introduced to learn the features of high-order neighbourhoods. Finally, a particular message-passing method is used to encode long-range information spanning the graph structure and realise accurate classification. Experiments on a test rig of helicopter tail-drive system are performed to verify the efficacy of the proposed method.