F. Lafont, N. Pessel, J. F. Balmat


This paper presents a new approach for the model-based diagnosis. The model is based on an adaptation with a variable forgetting factor. The variation of this factor is managed thanks to fuzzy logic. Thus, we propose a design method of a diagnosis system for the sensors defaults. In this study, the adaptive model is developed theoretically for the Multiple-Input Multiple-Output (MIMO) systems. We present the design stages of the fuzzy adaptive model and we give details of the Fault Detection and Isolation (FDI) principle. This approach is validated with a benchmark: a hydraulic process with three tanks. Different defaults (sensors) are simulated with the fuzzy adaptive model and the fuzzy approach for the diagnosis is compared with the residues method. The first results obtained are promising and seem applicable to a set of MIMO systems.


  1. Andersson, P., 1985. Adaptive forgetting in recursive identification through multiple. In. Proc. Int. J. Control, pp. 1175-1193.
  2. Campi, M., 1994. Performance of RLS Identification Algorithms with Forgetting Factor: A F-Mixing Approach, Journal of Mathematical Systems, Estimation, and Control, Vol. 4, N° 3, pp. 1-25.
  3. Carrasco, E. F., Rodriguez, J., Punal, A., Roca, E., Lema, J. M., 2004. Diagnosis of acidification states in an anaerobic wastewater treatment plant using a fuzzybased expert system, Control Engineering Practice, 12, pp. 59-64.
  4. Evsukoff, A., Gentil, S., Montmain, J., 2000. Fuzzy reasoning in co-operative supervision systems, Control Engineering Practice, 8, pp. 389-407.
  5. Fink, A., Fischer, M., Nelles, O., 2000. Supervision of Non-linear Adaptive Controllers Based on Fuzzy Models, Control Engineering Practice, 8(10), pp. 1093-1105.
  6. Isermann, R., 1984. Process fault detection based on modelling and estimation methods - A survey, Automatica,vol. 20, n°4, pp. 387-404.
  7. Isermann, R., 1997. Supervision, fault-detection and faultdiagnosis methods-Advanced methods and applications, Proc. Of the IMEKO world congress, New Measurements - Challenges and Visions, Tampere, Finland, vol. 1, n°4, pp. 1-28.
  8. Isermann, R., 2005. Model-based fault detection and diagnosis - Status and applications, Annual Reviews in Control, Elsevier Ltd., pp. 71-85, Vol. 28, No. 1.
  9. Jager, R., 1995. Fuzzy Logic in Control, Thesis Technische Universiteit Delft, ISBN 90-9008318-9.
  10. Jamouli, H., 2003. Génération de résidus directionnels pour le diagnostic des systèmes linéaires stochastiques et la commande tolérante aux fautes, Thesis, University Henri Poincaré, Nancy 1.
  11. Kroll, A., 1996. Identification of functional fuzzy models using multidimensional reference fuzzy sets, Fuzzy Sets & Systems, vol. 8, pp. 149-158.
  12. Lafont, F., Balmat, J. F., Taurines, M., 2005. Fuzzy forgetting factor for system identification, Third International Conference on Systems, Signals & Devices, Volume 1, Systems analysis & Automatic Control, Sousse, Tunisia, March 21-24.
  13. Liu, G., Toncich, D. J., Harvey, E. C., Yuan, F., 2005. Diagnostic technique for laser micromachining of multi-layer thin films, International Journal of Machine Tools & Manufacture, 45, pp. 583-589.
  14. Maquin, D., 1997. Diagnostic à base de modèles des systèmes technologiques, Mémoire d'Habilitation à Diriger des Recherches, Institut National Polytechnique de Lorraine.
  15. Noura, H., 2002. Méthodes d'accommodation aux défauts: théorie et application, Mémoire d'Habilitation à Diriger des Recherches, University Henri Poincaré, Nancy 1.
  16. Querelle, R., Mary, R., Kiupel, N., Frank, P. M., 1996. Use of qualitative modelling and fuzzy clustering for fault diagnosis, Proc. of world Automation Congress WAC'96, Montpellier, France, vol. 5, n°4, pp. 527- 532.
  17. Ripoll, P., 1999. Conception d'un système de diagnostic flou appliqué au moteur automobile, Thesis, the University of Savoie.
  18. Sala, A., Guerra, T. M., Babuska, R., 2005. Perspectives of fuzzy systems and control, Fuzzy Sets & Systems, 156, pp. 432-444.
  19. Slama-Belkhodja, I., de Fornel, B., 1996. Commande adaptative d'une machine asynchrone, J. Phys. III, Vol. 6, pp. 779-796.
  20. Szederkényi, G., 1998. Model-Based Fault Detection of Heat Exchangers, department of Applied Computer Science University of Veszprém.
  21. Trabelsi, A., Lafont, F., Kamoun, M., Enéa, G., 2004. Identification of non-linear multi-variable systems by adaptive Fuzzy Takagi-Sugeno model, International Journal of Computational Cognition, ISBN 1542- 5908, vol. 2, n° 3, pp. 137-153.
  22. Uhl, T., 2005. Identification of modal parameters for nonstationary mechanical systems, Arch Appl Mech, 74, pp. 878-889.

Paper Citation

in Harvard Style

Lafont F., Pessel N. and F. Balmat J. (2007). A FUZZY PARAMETRIC APPROACH FOR THE MODEL-BASED DIAGNOSIS . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 25-31. DOI: 10.5220/0001620100250031

in Bibtex Style

author={F. Lafont and N. Pessel and J. F. Balmat},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},

in EndNote Style

JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
SN - 978-972-8865-82-5
AU - Lafont F.
AU - Pessel N.
AU - F. Balmat J.
PY - 2007
SP - 25
EP - 31
DO - 10.5220/0001620100250031