Online quantitative safety monitoring approach for unattended train operation system considering stochastic factors

Ruijun Cheng, Yu Cheng, Dewang Chen and Haifeng Song

Reliability Engineering and System Safety, 2021, vol. 216, issue C

Abstract: Online safety monitoring is the key technology to the realize unattended train operation (UTO). So, online quantitative safety monitoring method is proposed to solve the state space explosion problem of the traditional model checking method. The quantitative safety level is defined to quantitatively describe the safety level of the operational state of UTO. To begin with, the composite transition graph of the linear hybrid automata (LHA) of train tracking control and the probabilistic hybrid automata (PHA) model of moving block control principles is constructed based on the composition rules between hybrid automata. Then, the reachable probability distribution of dangerous states can be obtained by verifying the established transition graph with abundant simulation results. Furthermore, the safety constrained boundary of the selected stochastic parameters in bounded time can be achieved for the corresponding quantitative safety level by using the proposed Safety Constraint Computation Algorithm. Finally, based on the performances of stochastic events evaluated by hybrid automata online, the safety status of UTO can be quantitatively monitored in real-time.

Keywords: Unattended train operation (UTO); Quantitative safety verification; Probabilistic hybrid automata (PHA); Probabilistic reachable set analysis; Online quantitative safety monitoring (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:216:y:2021:i:c:s095183202100449x

DOI: 10.1016/j.ress.2021.107933

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