StatAvaries: An original multinet decision support tool for evaluating rail maintenance strategies

International audienceReliability analysis is an integral part of system design and operating. Moreover, it can be an input to optimize maintenance policies. Recently, Dynamic Bayesian Networks (DBN) have been proved relevant to represent complex systems and perform reliability studies. The major drawback of this approach comes from the constraint on the sojourn times which are necessarily exponentially distributed, as in usual Markovian approaches. To avoid this constraint, a new formalism named Graphical Duration Models (GDM) was introduced¹. This approach, based on semi- Markovian models, allows representing all kind of sojourn time distributions. Then, the degradation process of complex systems (multi-components, multi-states, eventually influenced by contextual variables) can be accurately modeled and thus, the related reliability indicators correctly estimated. With this generic approach (named VirMaLab, for Virtual Maintenance Laboratory) various industrial applications were developed, especially as decision support tools for the optimization of railway infrastructure maintenance strategies