Reliability analysis has become an integral part of system design and operating. This is especially true for systems performing critical tasks such as mass transportation systems. This explains the numerous advances in the field of reliability modeling. More recently, some studies involving the use of Bayesian Networks (BN) have been proved relevant to represent complex systems and perform reliability studies. In previous works, the generic decision support tool VirMaLab, developed to evaluate complex systems maintenance strategies, was introduced. This approach is based on a specific Dynamic BN, designed to model stochastic degradation processes and allowing any kind of state sojourn distributions along with an accurate context description: the Graphical Duration Models. This paper deals with a multi-nets extension of VirMaLab, dedicated to maintenance of metro rails. Indeed, due to fulfillment of high-performance levels of safety and availability (the latter being especially critical at peak hours), the operator needs to estimate, hour by hour its ability to detect broken rails.
