A Bayesian network to evaluate underground rails maintenance strategies in an automation context

Reliability analysis has become an integral part of system design and operation. 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 have been proven relevant to represent complex systems and perform reliability studies. In previous works, a generic methodology was introduced for developing a decision support tool to evaluate complex systems maintenance strategies. This article deals with development of such a decision tool dedicated to the maintenance of Paris metro rails. Indeed, owing to fulfillment of high-performance levels of safety and availability (the latter being especially critical at peak hours), operators need to estimate, hour by hour their ability to prevent or to detect broken rails. To address this problem, a decision support tool was developed, the aim of this article is to evaluate, compare and optimize various operating and maintenance strategies.