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Despite the existence of the multitude of behavioral analysis tools for industrial systems, increasingly comp le x, managers to date find difficulties to define maintenance strategies able to significantly improve the overall performance of companies in terms of production, quality, safety and environment. A static maintenance and not adapted to the evolution of the state system does not meet the expectations of industrialists. However, the behavior of any degradable system is closely related to the state of its co mponents. This random influence is not always sufficiently considered for various reasons, consequently any decision making remains subjective. Our approach based on dynamic Bayesian networks (DBN) consists has the modeling of the system and the functional dependencies of its co mponents. The results obtained then, after the introduction in the model of the most appropriate actions of maintenance show all the importance of this technique and the possible applicat ions.

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