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In this paper, we are interested in the monitoring of complex systems modeled by functional graphs. We propose a diagnostic algorithm to isolate and identify the causes of failure located at a node of the functional graph. We establish hypotheses, first, on the choice of type of function who causes failure, and second, on the choice of the number of function who are directly observable. The key contribution of our approach is the establishment of behavioral models based on T-timed Petri Nets to refine the diagnosis based on functional graphs and to finally isolate faults. This study proposes first a generic model for representing the behavior of nodes in the functional graph according to a number of rules. These rules are designed to combine the partial models to form a global model based on a functional graph structure.