Monitoring and Predicating Accidents for Interlocking Systems Based on SHA
Yan Wang, Xiaohong Chen · 2018
As one of the core subsystems of the rail transit system, the safety of the interlocking system should be the goal of the development. The accident prediction of interlocking system is an important way to ensure the safety. Different from our previous discrete model based prediction approach, in this paper, considering the characteristics of equipment fault stochasticity, real time and continuous physical environment, we propose an approach to build hybrid system models for interlocking systems using stochastic hybrid automata (SHA), constructing monitor system and predicting accidents in interlocking systems. The main contributions include: (1) An accident prediction framework for interlocking system based on SHA is constructed; and (2) the hybrid system models and accident prediction model of interlocking systems are presented; and (3) an automatic generation algorithm of the monitor model is designed and implemented. Finally, a case study is presented to illustrate the feasibility and effectiveness of our approach, and an evaluation is carried out to compare the advantages of this approach with the previous discrete prediction approach.