A Joint Observer-Based Distributed Fault Detection Method for Traction Systems of High-Speed Trains

The past two decades have witnessed the development of fault detection (FD) methods in the field of high-speed trains. This trend is the consequence of the increasing requirement for safe and reliable running of mechanical systems in trains. Despite numerous reported methods, data-driven frameworks require further improvement in analyzing dependencies of multi-source variables on system states to enhance distributed fault detection performance in trains. In this paper, the joint observer-based distributed FD scheme for the dynamic process is investigated for traction control systems of high-speed trains. To be specific, this paper proposed a joint observer model via joint functions aiming at improving the analysis ability for multi-source signals. Then, the existence of the joint state and output observer is proved. Additionally, to enhance the detection ability of traction systems, the data-driven distributed FD method is proposed based on subspace identification and the average consensus method. Finally, numerical simulations and physical experiments were conducted to validate the efficacy of the proposed method. Comparative results demonstrate superior dynamic performance of the proposed method, achieving false alarm rates (FAR) and missed alarm rates (MAR) below 1% in high-speed train traction systems.