Modélisation des procédés à partir d'observations datées

International audience; This paper presents a modelling approach of dynamic process for diagnosis that is compatible with the Stochastic Approach framework for discovering temporal knowledge from the timed observations contained in a database. The motivation is to define a multi-model formalism able to represent both the knowledge of these two sources. The aim is to model the process at the same level of abstraction that an expert uses to diagnose the process. The underlying idea is that at this level of abstraction, the model is simple enough to allow an efficient diagnosis. The proposed formalism represents the knowledge in four models: a structural model defining the components and the connection relations of the process, a behavioural model defining the states and the transition states of the process, a functional model containing the logical relations between the values of the process variables, which are defined in the perception model. The models are linked with the process variables. This point facilitates the analysis of the four models consistency and is the basis of the corresponding knowledge modelling methodology. The formalism and methodology are illustrated with the model of a hydraulic dam in Cublize (France).