Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Philippe Weber (1) , Lionel Jouffe (2)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 BAYESIA - Bayesia

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.

Mots clés: Dynamic Object Oriented Bayesian Networks (DOOBNs); Markov Chain; Reliability estimation

Philippe Weber, Lionel Jouffe. Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliability Engineering and System Safety, 2006, 91(2), pp.149-162. ⟨10.1016/j.ress.2005.03.006⟩. ⟨hal-00021293⟩