Marc Le Goc, Emilie Masse, Corinne Curt



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 that is 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 transitions states of the process, a functional model containing the logical relations between the values of the process’s variables, which are defined in the perception model. The models are linked with the process’s variables. This point facilitates the analysis of the consistency of the four models and is the basis of the corresponding knowledge modelling methodology. The formalism and the methodology is illustrated with the model of a hydraulic dam of Cublize (France).


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Paper Citation

in Harvard Style

Le Goc M., Masse E. and Curt C. (2008). MODELING PROCESSES FROM TIMED OBSERVATIONS . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8111-51-7, pages 249-256. DOI: 10.5220/0001884502490256

in Bibtex Style

author={Marc Le Goc and Emilie Masse and Corinne Curt},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,},

in EndNote Style

JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,
SN - 978-989-8111-51-7
AU - Le Goc M.
AU - Masse E.
AU - Curt C.
PY - 2008
SP - 249
EP - 256
DO - 10.5220/0001884502490256