Dynamic Fault Detection Method of Traction Systems in High-Speed Trains Based on Joint Observer

This paper proposes a joint state observer based on deep learning for traction systems in high-speed trains. For actual systems, the signal collected by multiple sensors contains different variables, and each variable will reflect the state of the systems. However, most researchers construct the state estimation strategy without consideration of relations between variables, reducing the accuracy of fault detection. Therefore, how to analyze the correlation of different variables and design the data-driven observer becomes the difficult problem. This paper designs a data-driven joint output observer for traction system of high-speed trains. The joint distribution function is constructed by the marginal distribution of different variables and the resultant weight of the joint model is calculated by Kendall rank correlation coefficient. In the end, the proposed method is verified on a pilot-scale experimental platform and traction systems in high-speed trains.