Toward a decompositional and incremental approach to diagnosis of dynamic systems from timed observations

[Departement_IRSTEA]Eaux [TR1_IRSTEA]RIVAGE; International audience; It is now well-known that the size of the model is the bottleneck when using model-based approaches to diagnose complex systems. To answer this problem, decompositional and multi modelling approaches have been proposed. In this paper, we propose a multi-modelling method called TOM4D (Timed Observations Modelling for Diagnosis) able to cope with dynamic aspects. It relies on four models: perception, structural, functional and behaviour models. The behaviour model is described through system component models as a set of component behaviour models and the global diagnosis is computed from the component diagnoses (also called local diagnoses). Another problem, which is far less considered, is the size of the diagnosis itself. However, it can also be huge enough, especially when dealing with dynamic system. To solve this problem, we propose in this paper to use The Timed Observation Theory. In this context, we characterize the diagnosis using TOM4D and the timed observation theory. We show their relevance to get a tractable representation of diagnosis. To illustrate the impact on the diagnosis size, experimental results on a hydraulic example are given.