Intelligent Quantitative Safety Monitoring Approach for ATP Using LSSVM and Probabilistic Model Checking Considering Imperfect Fault Coverage

Online safety monitoring is the key technology to realize the safe operation of the automatic train protection (ATP) system. So, based on the probabilistic model checking and least square support vector machine (LSSVM) algorithms, an intelligent quantitative safety monitoring method is proposed to monitor the operational safety of ATP online. To begin with, the dynamic fault tree (DFT) model and continuous-time Markov Chains (CTMC) model of ATP are established based on the fault-tolerant structure of ATP. Then, the reliability and safety performance of DFT are evaluated by the hierarchical iterative evaluation method when considering the imperfect fault characteristics of the critical sub-equipment. Furthermore, continuous stochastic logic (CSL) is introduced to represent the temporal quantitative safety property. For the defined CSL property, the CTMC model will be verified by probabilistic model checking off-line, and the verification data set will be obtained. The distribution regularities of maximum reachable probability about the failure rate parameters of sub-equipment will be achieved by training the obtained verification data set with the LSSVM model. Finally, the quantitative safety boundaries (QSBs) of the corresponding quantitative safety levels are computed by the designed algorithm. The obtained QSBs can be used for monitoring the operational status of ATP online.