LAUSR

The bogie system is a critical system for a high-speed train, which is composed of various mechanical parts. Therefore, the health of the bogie can directly affect the health of high-speed train. Temperature signals, speed signals and pressure signals are collected from the bogie can reflect its health. Hence, the multi-sensor fault diagnosis methods can provide novel solutions for the bogie health monitoring tool. This paper presents a novel bogie fault diagnosis scheme named the AttGGCN model, using graph convolutional network (GCN), gated recurrent unit (GRU) and attention mechanism. In this fault diagnosis scheme, the bogie data network is established firstly. Then, temporal and spatial features are extracted and fused using GCG unit. Finally, the GCN are used for fault identification. Twenty-four kinds of measured signals and seven types of faults from actual High-speed train in operation are utilized for verification. Results show that the AttGGCN model has the highest accuracy compared to conventional models. In addition, experiments on different scales of training sets suggest that the AttGGCN model has strong robustness in small-scale datasets. Besides, ablation experiments certificate that the attention mechanism is able to strengthen the feature extraction ability.