  Maintenance plays now a critical role in manufacturing for achieving
important cost savings and competitive advantage while preserving product
conditions. It suggests moving from conventional maintenance practices to
predictive strategy. Indeed the maintenance action has to be done at the right
time based on the system performance and component Remaining Useful Life (RUL)
assessed by a prognostic process. In that way, this paper proposes a
methodology in order to evaluate the performance loss of the system according
to the degradation of component and the deviations of system input flows. This
methodology is supported by the neuro-fuzzy tool ANFIS (Adaptive Neuro-Fuzzy
Inference Systems) that allows to integrate knowledge from two different
sources: expertise and real data. The feasibility and added value of such
methodology is then highlighted through an application case extracted from the
TELMA platform used for education and research.
