In the railway transport, rolling stock cost and availability are major concern. To optimise the maintenance cost of the railway transport system, one solution consists in better detecting and diagnosing failures. Today, centralized monitoring/diagnosis architectures reach their limits. Innovation is therefore necessary. This technological innovation may be implemented with embedded distributed and communicating monitoring/diagnosis architectures in order to faster detect and localize failures and to make a validation with respect to the train operational context.The present research work, carried out as part of the SURFER FUI project (french acronym standing for railway active monitoring) lead by Bombardier, aim to propose a methodology to assess dependability of monitoring/diagnosis architectures. To this end, a caracterisation et une modélisation génériques des monitoring/diagnosis architectures based on the stochastic Petri Nets have been proposed. These generic models take into account communication networks (and the associated failure modes), which constitutes a central point of the studied monitoring/diagnosis architectures. The proposed models have been edited and theoretically validated by simulation. A sensitiveness of the monitoring/diagnosis architectures to parameters has been studied. Finally, these generic models have applied to a real case of the railway transport, train passenger access systems, which are critical in term of availability and diagnosability.
