TOWARDS A MULTIMODELING APPROACH OF DYNAMIC SYSTEMS FOR DIAGNOSIS

Marc Le Goc, Emilie Masse

2007

Abstract

This paper presents the basis of a multimodeling methodology that uses a CommonKADS conceptual model to interpret the diagnosis knowledge with the aim of representing the system with three models: a structural model describing the relations between the components of the system, a functional model describing the relations between the values the variables of the system can take (i.e. the functions) and a behavioural model describing the states of the system and the discrete events firing the state transitions. The relation between these models is made with the notion of variable: a variable used in a function of the functional model is associated with an element of the structural model and a discrete event is defined as the affectation of a value to a variable. This methodology is presented in this paper with a toy but pedagogic problem: the technical diagnosis of a car. The motivating idea is that using the same level of abstraction that the expert can facilitate the problem solving reasoning.

References

  1. Basseville, M. and Cordier, M-O., 1996. Surveillance et diagnostic de systèmes dynamiques : approches complémentaires du traitement de signal et de l'intelligence artificielle, office publication.
  2. Bouché, P., Le Goc, M., and Giambiasi, N., 2005. Modeling Discrete Event Sequences for Discovering Diagnosis Signatures, Summer Computer Simulation Conference, SCSC'05, Philadelphia, USA.
  3. Clancey, W., 1985. Heuristic classification, Artificial Intelligence Journal 25(3) 289-350.
  4. Cordier, M.O., Dousson, C., 2000. « Alarm driven monitoring based on chronicles », Proceedings of SafeProcess 2000, pp.286-291, Budapest, Hungary.
  5. Chittaro, L., Guida, G., Tasso, C., and Toppano, E., 1993. Functional and teleological knowledge in the multimodeling approach for reasoning about physical systems: a case study in diagnosis. IEEE transactions on systems.
  6. Dagues, P., 2001. « Théorie logique du diagnostic à base de modèles », in Diagnostic, intelligence artificielle et reconnaissance des formes, Hermes, p. 17-105.
  7. Le Goc, M., Bouché, P. and Giambiasi, N., 2005. Stochastic modeling of continuous time discrete event sequence for diagnosis. Proceedings of the 16th International Workshop on Principles of Diagnosis, DX-05, Pacific Grove, California, USA.
  8. Le Goc, M., Bouché, P., Giambiasi, N., 2006. DEVS, a formalism to operationnalize chronicle models in the ELP Laboratory. Proceedings of DEVS'06, DEVS Integrative M&S Symposium, Part of the 2006 Spring Simulation Multiconference (SpringSim'06), pp. 143- 150, Van Braun Convention, Huntsville, Alabama, USA, April 2-6 2006.
  9. Masse, E., and Le Goc, M., 2007. Modeling Dynamic Systems from their Behavior for a Multi Model Based Diagnosis. To appear in the proceedings of the 18th International Workshop on the Principles of Diagnosis (DX'07), Nashville, USA, Mai 29th to 31th 2007.
  10. Reiter, R., 1987. A theory for Diagnosis from First Principles, Artificial Intelligence 32, P 57-95.
  11. Schreiber, A. Th., Akkermans, J. M., Anjewierden, A. A., de Hoog, R., Shadbolt, N. R., Van de Velde W., Wielinga B. J., 2000. Publication, Knowledge Engineering and Management, The CommonKADS methodology, MIT Press.
  12. Thetiot, R., 1999. PhD in Sciences, Utilisation de l'approche multi-modèles pour l'aide au diagnostic d'installations industrielles, Université d'Evry Val d' Essonne.
  13. Zanni, C., Le Goc, M., Frydman, C., 2005. Publication, A conceptual framework for the analysis, Classification and choice of knowledge-based diagnosis systems, International Journal of Knowledge-Based & Intelligent Engineering Systems (KES Journal), IOS Press Eds., 41 p.
  14. Zouaoui, F., 1998. PhD in Sciences, Aide à l'interprétation du fonctionnement des systèmes physiques en utilisant une approche multi-modèles. Application au circuit primaire d'une centrale à eau pressurisée, Université de Paris XI - Orsay.
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Paper Citation


in Harvard Style

Le Goc M. and Masse E. (2007). TOWARDS A MULTIMODELING APPROACH OF DYNAMIC SYSTEMS FOR DIAGNOSIS . In Proceedings of the Second International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8111-05-0, pages 277-282. DOI: 10.5220/0001341702770282


in Bibtex Style

@conference{icsoft07,
author={Marc Le Goc and Emilie Masse},
title={TOWARDS A MULTIMODELING APPROACH OF DYNAMIC SYSTEMS FOR DIAGNOSIS},
booktitle={Proceedings of the Second International Conference on Software and Data Technologies - Volume 1: ICSOFT,},
year={2007},
pages={277-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001341702770282},
isbn={978-989-8111-05-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Software and Data Technologies - Volume 1: ICSOFT,
TI - TOWARDS A MULTIMODELING APPROACH OF DYNAMIC SYSTEMS FOR DIAGNOSIS
SN - 978-989-8111-05-0
AU - Le Goc M.
AU - Masse E.
PY - 2007
SP - 277
EP - 282
DO - 10.5220/0001341702770282