Abstract
Health monitoring and fault diagnosis of a high-speed train is an important research area in guaranteeing the safe and long-term operation of the high-speed railway. For a multichannel health monitoring system, a major technical challenge is to extract information from different channels with divergence patterns as a result of distinct types and layout of sensors. To this end, this paper proposes a novel group convolutional network based on synchrony information. The proposed method is able to gather signals with similar patterns and process these channels with specific groups of neurons while simultaneously assigning signals with significant difference to different groups. In this approach, the feature can be extracted more effectively and the performance can be improved, owing to the sharing of filters for similar patterns. The effectiveness of the method is validated on high-speed train fault dataset. Experiments show that the proposed model performs better than normal convolutions and normal group convolutions on this task, which achieves an accuracy of 98.27% (sigma = 1.73) with good computational efficiency.