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Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 453169, 10 pages
Early FDI Based on Residuals Design According to the Analysis of Models of Faults: Application to DAMADICS
1Department of Control Engineering, University of Mohamed Khider, Biskra 07000, Algeria
2Electrical Engineering and Automatic Control Research Group (GREAH), University of Le Havre, 25 rue Philippe Lebon, 76058 Le Havre, France
3Department of Electronics, University of Badji Mokhtar, Annaba 23000, Algeria
Received 10 May 2011; Revised 29 June 2011; Accepted 19 July 2011
Academic Editor: Paolo Gastaldo
Copyright © 2011 Yahia Kourd et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- J. Korbicz, J. M. Koscielny, Z. Kowalczuk, and W. Cholewa, Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin, Germany, 2004.
- R. J. Patton and J. Chen, “On eigenstructure assignment for robust fault diagnosis,” International Journal of Robust and Nonlinear Control, vol. 10, no. 14, pp. 1193–1208, 2000.
- H. R. Scola, R. Nikoukah, and F. Delebecque, “Test signal design for failure detection: a linear programming approach,” International Journal of Applied Mathematics and Computer Science, vol. 13, no. 4, pp. 515–526, 2003.
- M. Witczak, Modeling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems. From Analytical to Soft Computing Approaches, Springer, Berlin, Germany, 2007.
- E. Delaleau, J. P. Louis, and R. Ortega, “Modeling and control of induction motors,” International Journal of Applied Mathematics and Computer Science, vol. 11, no. 1, pp. 105–129, 2001.
- T. Soderstrom, et al., System Identification, Prentice-Hall International, Hemel Hempstead, Hertfordshire, UK, 1989.
- T. Bouthiba, “Fault location in ehv transmission lines using artificial neural networks,” International Journal of Applied Mathematics and Computer Science, vol. 14, no. 1, pp. 69–78, 2004.
- M. Mrugalski, M. Witczak, and J. Korbicz, “Confidence estimation of the multi-layer perceptron and its application in fault detection systems,” Engineering Applications of Artificial Intelligence, vol. 21, no. 6, pp. 895–906, 2008.
- M. M. Gupta, Static and Dynamic Neural Networks, John Wiley & Sons, Hoboken, NJ, USA, 2003.
- O. Nelles, Non-Linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models, Springer, Berlin, Germany, 2001.
- S. C. Tan, C. P. Lim, and M. V. C. Rao, “A hybrid neural network model for rule generation and its application to process fault detection and diagnosis,” Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 203–213, 2007.
- V. Uraikul, C. W. Chan, and P. Tontiwachwuthikul, “Artificial intelligence for monitoring and supervisory control of process systems,” Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 115–131, 2007.
- J. Vieira, F. M. Dias, and A. Mota, “Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study,” Engineering Applications of Artificial Intelligence, vol. 17, no. 3, pp. 265–273, 2004.
- B. Michal, R. Patton, M. Syfert, S. de las Heras, and J. Quevedo, “Introduction to the DAMADICS actuator FDI benchmark study,” Control Engineering Practice, vol. 14, no. 6, pp. 577–596, 2006.
- R. Isermann, “Model-based fault-detection and diagnosis—status and applications,” Annual Reviews in Control, vol. 29, no. 1, pp. 71–85, 2005.
- R. Isermann, Fault-Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Springer, Berlin, Germany, 2006.
- J. C. Yang and D. W. Clarke, “The self-validating actuator,” Control Engineering Practice, vol. 7, no. 2, pp. 249–260, 1999.
- M. Henry, “Plant asset management via intelligent sensors digital, distributed and for free,” Computing and Control Engineering Journal, vol. 11, no. 5, pp. 211–213, 2000.
- M. Tombs, “Intelligent and self-validating sensors and actuators,” Computing and Control Engineering Journal, vol. 13, no. 5, pp. 218–220, 2002.
- J. B. Gomm, D. L. Yu, and D. Williams, “Sensor fault diagnosis in a chemical process via RBF neural networks,” Control Engineering Practice, vol. 7, no. 1, pp. 49–55, 1999.
- L. Yaguol, H. Zhengjia, and Z. Yahyang, “A new approach to intelligent fault diagnosis of rotating machinery,” Expert Systems with Applications, vol. 35, no. 4, pp. 1593–1600, 2008.
- H. T. Mok and C. W. Chan, “Online fault detection and isolation of nonlinear systems based on neurofuzzy networks,” Engineering Applications of Artificial Intelligence, vol. 21, no. 2, pp. 171–181, 2008.
- J. Zhao, J. Huang, and W. Sun, “On-line early fault detection and diagnosis of municipal solid waste incinerators,” Waste Management, vol. 28, no. 11, pp. 2406–2414, 2008.
- H. Hjalmarsson, A. Juditsky, J. Sjöberg et al., “Nonlinear black-box modeling in system identification: a unified overview,” Automatica, vol. 31, no. 12, pp. 1691–1724, 1995.
- Y. Kourd, N. Guersi, and D. Lefebvre, “A two stages diagnosis method with neuronal networks,” in Proceedings of the International Conference on Electrical Engineering Design and Technologies (ICEEDT '08), Hammamet, Tunisie, 2008.
- Y. Kourd, N. Guersi, and D. Lefebvre, “A two stages diagnosis method with Neuro-fuzzy approach,” in Proceedings of the 6th Conférence Internationale Francophone d'Automatique, Nancy, France, 2010.
- Y. Kourd, N. Guersi, and D. Lefebvre, “Neuro-fuzzy approach for fault diagnosis: application to the DAMADICS,” in Proceedings of the 7th international Conference on Informatics in Control, Automation and Robotics (ICINCO '10), Funchal, Madeira, Portugal, 2010.
- M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault Tolerant Control, Springer, New York, NY, USA, 2003.
- D. Lefebvre, H. Chafouk, and M. Lebbal, Modélisation et Diagnostic des Systèmes. Une Approche Hybride, Éditions universitaires européennes, 2010.
- K. Patan and T. Parisini, “Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process,” Journal of Process Control, vol. 15, no. 1, pp. 67–79, 2005.
- J. M. Kościelny, M. Bartyś, P. Rzepiejewski, and J. Sá da Costa, “Actuator fault distinguishability study for the DAMADICS benchmark problem,” Control Engineering Practice, vol. 14, no. 6, pp. 645–652, 2006.
- DAMADICS, 2002, http://diag.mchtr.pw.edu.pl/damadics/.