Nowadays, the importance of the maintenance function has increased, due to the requirements on the maintain in operational conditions phase (MCO) of the system-of-interest (SI). As well as for the relevant role of maintenance in improving availability, performance efficiency, total plant availability, etc. To control performances, maintenance managers should be able to make some choices about the maintenance strategies and the resources that can fulfil the requirements. Within this context, we propose a methodology to formalize a model allowing to perform simulation to assess maintenance strategies. The scientific contribution of our work is that this approach unify by using a probabilistic relational model (PRM), different kind of knowledge needed to assess maintenance strategies. Knowledge is presented as generic and modular patterns based on PRM. These patterns integrate relevant decisional variables of the system of interest and of its maintenance system. This approach eases the modeling phase for a specific application. This methodology is one of the results of the project ANR SKOOB. This approach was tested on an industrial case for the maintenance of a harvest production process.
