Abstract
Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M.: Diagnosis and Fault-Tolerant Control. Springer, New York (2003)
Chen, J., Patton, R.J.: Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer, Berlin (1999)
Frank, P.M., Köppen-Seliger, B.: New developments using AI in fault diagnosis. Engineering Applications of Artificial Intelligence 10(1), 3–14 (1997)
Gertler, J.: Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York (1998)
Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Iserman, R.: Fault Diagnosis Systems. In: An Introduction from Fault Detection to Fault Tolerance. Springer, New York (2006)
Köppen-Seliger, B., Frank, P.M.: Fuzzy logic and neural networks in fault detection. In: Jain, L., Martin, N. (eds.) Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms, pp. 169–209. CRC Press, New York (1999)
Korbicz, J.: Fault detection using analytical and soft computing methods. Bulletin of the Polish Academy of Sciences:Technical Sciences 54(1), 75–88 (2006)
Korbicz, J., Kościelny, J., Kowalczuk, Z., Cholewa, W.: Fault Diagnosis. In: Models, Artificial Intelligence, Applications. Springer, Berlin (2004)
Korbicz, J., Patan, K., Kowal, M. (eds.): Fault Diagnosis and Fault Tolerant Control. Academic Publishing House EXIT, Warsaw (2007)
Marcu, T., Mirea, L., Frank, P.M.: Development of dynamical neural networks with application to observer based fault detection and isolation. International Journal of Applied Mathematics and Computer Science 9(3), 547–570 (1999)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 12–18 (1990)
Norgard, M., Ravn, O., Poulsen, N., Hansen, L.: Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)
Patan, K., Korbicz, J., GÅ‚owacki, G.: DC motor fault diagnosis by means of artificial neural networks. In: Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007, Angers, France. Published on CD-ROM (2007)
Patan, K., Parisini, T.: Identification of neural dynamic models for fault detection and isolation: The case of a real sugar evaporation process. Journal of Process Control 15(1), 67–79 (2005)
Puig, V., Stancu, A., Escobet, T., Nejjari, F., Quevedo, J., Patton, R.J.: Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem. Control Engineering Practice 14(6), 621–633 (2006)
Rodrigues, M., Theilliol, D., Aberkane, S., Sauter, D.: Fault tolerant control design for polytopic LPV systems. International Journal of Applied Mathematics and Computer Science 17(1), 27–37 (2007)
Rutkowski, L.: New Soft Computing Techniques for System Modelling, Pattern Classification and Image Processing. Springer, Berlin (2004)
Witczak, M.: Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems. Springer, Berlin (2007)
Witczak, M., Korbicz, J., Mrugalski, M., Patton, R.J.: A GMDH neural network-based approach to robust fault diagnosis: Application to the DAMADICS benchmark problem. Control Engineering Practice 14(6), 671–683 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcia, R.F., De Miguel Catoira, A., Sanz, B.F. (2010). FDI and Accommodation Using NN Based Techniques. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-13769-3_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
eBook Packages: Computer ScienceComputer Science (R0)