Research On Dynamic Fault Tree And Monte Carlo Based Risk Analysis Of Train Control System

ABSTRACT:Train control system, a safety-critical system for guaranteeing train operation safety and reliability, obtains rapid development due to the advanced computer technology, control technology and electronic technology. However, with the continuous incensement of train speed and operation density, it becomes more important to ensure the safety of train control system. In order to reduce risk and ensure train operation safety, risk analysis and assessment of train control system has become an integral part of system research. In this fields, scholars home and abroad make a huge effort to establish standards and related plans, put forward a number of theories and methods. Although they make a great contribution to the qualitative analysis, there is still room for quantitative safety analysis of train control systems.Because of the over-reliance on experts’experience and inadequate consideration on dynamic aspects of the train control system, this paper presents a new method for train control system risk analysis, which uses parameter estimation approach for statistical data of system operation to obtain components’dynamic performance and then uses these components performance knowledge to solve system’s dynamic fault tree by simulation.The main contents of this paper are listed as follows:Firstly, the estimated effect of common parameter estimation methods was compared. Considering the fact that train control system components’failure data feature is obtained hardly and exists some priori knowledge, we decided to adopt Bayesian maximum likelihood method to obtain system components sample of failure time.Secondly, we model train control system with dynamic fault tree for risk analysis. Because of large numbers of components and complex structure of the train control system, the Monte Carlo simulation approach was used, on the one hand, to solve dynamic fault tree, and on the other hand, to avoid state explosion.Finally, the definition of various components’sensitivity was presented to find its impact on system performance. Then a realization of new risk analysis method is proposed, taking computer interlocking system as an example. Using components failure data and dynamic fault tree model of the computer interlocking system, taking system failure as the main risk of the system, we get the risk probability range. In addition, we evaluated various components of the system with degree of importance. The complexity of the calculation, form and accuracy of the results demonstrate the algorithm excellent performance on train control system risk analysis...