Research on the fault prediction method of an on-board subsystem in high-speed train control systems

Abstract

The train control system is the “nerve center” of the high-speed train information system, which is large-scale and comprises various components. The on-board subsystem is the core of the train control system and is key to ensuring traffic safety and improving operating efficiency. Currently, the fault data processing methods of the on-board subsystem remain manual, which primarily realizes the fault detection and location. It is difficult to achieve the fault mechanism level, and fault prediction cannot be realized effectively. In this paper, the on-board subsystem structure and the fault disposal status were analyzed. The existing problems have been summarized, and some concepts and algorithms to predict faults were introduced. Based on the on-board subsystem structure and each modules performance parameters, the system-level fault prediction model was established. Based on the practical operational data sets, the fault prediction based on the Bayesian network was carried out and verified under 20, 200, 2000 and 20000 sets, respectively. The prediction accuracy was 5%, 27%, 92% and 96.3%, respectively, under the condition of 2000 data sets. The hidden Markov model and neural network-based fault prediction solutions were compared with the proposed method. The results demonstrate the advanced performance of the Bayesian network-based solution in system-level fault prediction.