Timed Fault Tree Models of the China Yongwen Railway Accident

Safety is an essential requirement for railway transportation. There are many methods that have been developed to predict, prevent and mitigate accidents in this context. All of these methods have their own purpose and limitations. This paper presents a new useful analysis technique: timed fault tree analysis. This method extends traditional fault tree analysis with temporal events and fault characteristics. Timed Fault Trees (TFTs) can determine which faults need to be eliminated urgently, and it can also provide a safe time window to repair them. They can also be used to determine the time taken for railway maintenance requirements, and thereby improve maintenance efficiency, and reduce risks. In this paper, we present the features and functionality of a railway transportation system based on timed fault tree models. We demonstrate the applicability of our framework via a case study of the China Yongwen line railway accident

An efficient algorithm for exact computation of system and survival signatures using binary decision diagrams

System and survival signatures are important and popular tools for studying and analysing the reliability of systems. However, it is difficult to compute these signatures for systems with complex reliability structure functions and large numbers of components. This paper presents a new algorithm that is able to compute exact signatures for systems that are far more complex than is feasible using existing approaches. This is based on the use of reduced order binary decision diagrams (ROBDDs), multidimensional arrays and the dynamic programming paradigm. Results comparing the computational efficiency of deriving signatures for some example systems (including complex benchmark systems from the literature) using the new algorithm and a comparison enumerative algorithm are presented and demonstrate a significant reduction in computation time and improvement in scalability with increasing system complexity