The complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. The 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. One of the main challenges of the Extended Enterprise is to maintain and to optimise the quality of the services delivered by industrial objects in a dynamic way along their life cycle. The purpose is to conceive decision aiding systems to maintain the system in operation. Nevertheless, most of the automated systems do not provide the means of intelligent interpretation of the information when great process disturbances have to be considered. Moreover, decisions can be taken without a perfect perception of state of the system. This partial perception argues in favour of using a probabilistic estimation of the system state. The Artificial Intelligence can be used to bring help in decision aiding systems of manufacturing processes.