A rollingstock door system’s dynamic maintenance strategies based on a sensitivity analysis through bayesian networks

Rony Rozas (1) , Laurent Bouillaut (1) , Patrice Aknin (1) , Allou Same (1) , Olivier Francois (1) , Guillaume Branger (2)

1 IFSTTAR/COSYS/GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée
2 Bombardier Transportation

In most industrial fields, and particularly in the railway industry, the optimization of maintenance policies has become a key issue. Dynamic Bayesian networks (DBN) have been proved as relevant to perform reliability analysis as they can easily represent complex systems behaviors. Based on this formalism, graphical duration models (GDM) were developed by (Donat, et al., 2009) to set all kind of sojourn time distributions for each state of the system. Unlike to some Markovian approaches that impose exponential behavior, this approach could better model the exact degradation dynamic of real industrial systems. But, what commonly happens when the degradation process suddenly changes? The operator has to face with an unexpected increasing number of severe defects (and then a strong drop of its availability). These changes are generally due to either new component, introduced in the system for obsolescence reasons, or to changing operating conditions. The aim of the study introduced in this paper, focusing on Dynamic Maintenance Strategies, is to detect these drifts and to evaluate their impacts on the system’s behavior.

Mots clés: MAINTENANCE

Domaines: Autre

Rony Rozas, Laurent Bouillaut, Patrice Aknin, Allou Same, Olivier Francois, et al.. A rollingstock door system’s dynamic maintenance strategies based on a sensitivity analysis through bayesian networks. 10th World Congress on Railway Research (WCRR 2013), Nov 2013, Sydney, Australia. 8p. ⟨hal-00863687⟩