Intelligent quantitative safety monitoring approach for ATP system by neural computing and probabilistic model checking

Online quantitative safety monitoring is the key technology for ensuring the operational safety of the automatic train protection (ATP) system for the future advanced train control system. However, the traditional formal verification method can only deal with the system of small scale since these verification algorithms exhaustively search all executable paths of formal models. Especially for the formal models with uncertain parameters, the problem of memory overflow will occur due to the state space explosion, which makes verification algorithms fail to converge. To solve this problem, an intelligent quantitative safety monitoring approach is proposed by integrating the probabilistic model checking method (PMCM) with neural network in this paper. To begin with, the instantiated continuous-time Markov Chains model is verified by PMCM offline. Then, the neural network is constructed and optimized based on the offline verification results. The quantitative safety boundaries (QSBs) for all quantitative safety levels are also computed by the designed algorithm. Furthermore, hierarchical iterative evaluation method is applied to efficiently evaluate the reliability performance of the ATP online. Finally, the quantitative safety level of ATP or its subsystem will be determined by both reliability performance and the previous QSBs or the neural network online.