CAUSAL REASONING IMPROVED BY FUZZY LOGIC FOR DIAGNOSIS OF BOND GRAPH MODELLED UNCERTAIN PARAMETERS SYSTEMS

Walid Bouallègue, Salma Bouslama Bouabdallah, Moncef Tagina

Abstract

In this paper, a method for on-line fault detection and isolation (FDI) of bond graph (BG) modelled uncertain parameters systems is proposed. In this case, we don’t have to calculate the Analytical Redundancy Relations (RRAs) since residuals are directly generated from the Diagnostic Bond Graph (DBG). Detection is based on fuzzy logic approach. For isolation, two methods exploiting the causal properties of the BG model are used: Fault Signature Matrix (FSM), and exoneration. A real simulation example is provided to show the efficiency of the proposed methods.

References

  1. Biswas, G., Koutsoukos, X., Bregon, A., Pulido, B. (2009). Analytic Redundancy, Possible Conflicts, and TCG-based Fault Signature Diagnosis applied to Nonlinear Dynamic Systems, Proceedings of the IFAC-Safeprocess, Barcelona, Spain.
  2. Bouallègue, W., Bouslama Bouabdallah, S., Tagina, M. (2010). Diagnosis of Bond Graph modelled uncertain parameters systems using residuals sensitivity, Proceedings of the IEEE International conference on Systems, Man, and Cybernetics, Turkey, 593-600.
  3. Bouslama Bouabdallah, S., Tagina, M. (2006). A fuzzy approach for fault detection and isolation of uncertain parameter systems and comparison to binary logic, IFAC, IEEE 3rd International Conference on Informatics in Control, Automation and Robotics, ICINCO 2006. Portugal, Intelligent Control Systems and Optimization, 98-106.
  4. Cordier, M. O., Dague, P., Dumas, M., Levy, F., Montmain, J., Staroswiecki, M., and Trave-Massuyes, L. (2004). Conflicts versus Analytical Redundancy 10000 10000 10000 10000 10000 10000 10000 Relations: a comparative analysis of the Model-based Diagnosis approach from the Artificial Intelligence and Automatic Control perspectives, IEEE Trans. on Systems, Man, and Cybernetics (Part B), 34(5), 2163- 2177.
  5. Dauphin-Tanguy, G. (2000). Les bond graphs, Hermès, Paris.
  6. Dauphin-Tanguy, G., Tagina, M. (2003). La méthodologie bond graph: Principes et applications, Centre de publications universitaires, Tunis.
  7. Djeziri, M. A. (2007). Diagnostic des Systèmes Incertains par l'Approche Bond Graph, Thesis, Ecole centrale of Lille France.
  8. Djeziri, M. A., Ould Bouamama, B., Merzouki, R. (2009). Modelling and robust FDI of steam generator using uncertain bond graph model, Journal of process control.
  9. Evsukoff A., Gentil S., Montmain J. (2000). Fuzzy reasoning in co-operative supervision systems, Control Engineering Practice, 389-407.
  10. Frank, P. M., Köppen-Seliger. B. (1997). Fuzzy logic and neural network applications to fault diagnosis, International Journal of Approximate Reasoning, Volume 16, Issue 1,67-88.
  11. Fagarasan, I., Ploix, S., Gentil, S. (2004). Causal Fault Detection and Isolation Based on a Set-Membership Approach, Automatica, volume 40, 2099-2110.
  12. Montmain, J., Gentil, S. (1999). Causal Modeling for Supervision, Intelligent Control/Intelligent Systems and Semiotics, Proceedings of the IEEE International Symposium.
  13. Samantaray A. K., Medjaher K., Ould Bouamama B., Staroswiecki M., Dauphin-Tanguy G. (2006). Diagnostic bond graphs for online fault detection and isolation. In Simulation Modelling Practice and Theory.
  14. Samantaray A. K., Ould Bouamama B. (2008). Modelbased Process Supervision: A Bond Graph Approach, Springer.
  15. Staroswiecki, M. (2000). Quantitative and qualitative model for fault detection and isolation, Mechanical Systems and Signal Processing, 2000.
  16. Tagina, M. (1995). L'application de la modélisation bond graph à la surveillance des systèmes complexes, Thesis, University of Lille1, France 1995.
  17. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., Yin, K. (2003.a). A review of process fault detection and diagnosis Part I: Quantitative model-based methods, Computers and Chemical Engineering 27, 293-311.
  18. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., Yin, K. (2003.b). A review of process fault detection and diagnosis Part II: Qualitative models and search strategies, Computers and Chemical Engineering 27, 313-326.
  19. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., Yin, K. (2003.c). A review of process fault detection and diagnosis Part III: Process history based methods, Computers and Chemical Engineering 27, 327-346.
Download


Paper Citation


in Harvard Style

Bouallègue W., Bouslama Bouabdallah S. and Tagina M. (2011). CAUSAL REASONING IMPROVED BY FUZZY LOGIC FOR DIAGNOSIS OF BOND GRAPH MODELLED UNCERTAIN PARAMETERS SYSTEMS . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8425-75-1, pages 59-66. DOI: 10.5220/0003537500590066


in Bibtex Style

@conference{icinco11,
author={Walid Bouallègue and Salma Bouslama Bouabdallah and Moncef Tagina},
title={CAUSAL REASONING IMPROVED BY FUZZY LOGIC FOR DIAGNOSIS OF BOND GRAPH MODELLED UNCERTAIN PARAMETERS SYSTEMS},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2011},
pages={59-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003537500590066},
isbn={978-989-8425-75-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - CAUSAL REASONING IMPROVED BY FUZZY LOGIC FOR DIAGNOSIS OF BOND GRAPH MODELLED UNCERTAIN PARAMETERS SYSTEMS
SN - 978-989-8425-75-1
AU - Bouallègue W.
AU - Bouslama Bouabdallah S.
AU - Tagina M.
PY - 2011
SP - 59
EP - 66
DO - 10.5220/0003537500590066