Robust fault detection using Zonotope-Based GMDH neural network

Advances in Intelligent Systems and Computing

Volumn 230, Issue , 2014, Pages 101-112

  Mrugalski, Marcin ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

GMDH neural networks; Robust fault detection; Zonotope based algorithm

Indexed keywords

DATA HANDLING; LINEAR SYSTEMS; UNCERTAINTY ANALYSIS;

DYNAMIC GROUPS; GMDH NEURAL NETWORK; MATHEMATICAL DESCRIPTIONS; MODEL UNCERTAINTIES; ROBUST FAULT DETECTION; SELF-ORGANISING; TUNNEL FURNACE; ZONOTOPES;

FAULT DETECTION;

EID: 84983150579     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-39881-0_8     Document Type: Conference Paper

References (27)

1. Ding, S.: Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer, Heidelberg (2008)
2. Korbicz, J., Kościelny, J., Kowalczuk, Z., Cholewa, W. (eds.): Fault diagnosis. Models, Artificial Intelligence, Applications. Springer, Berlin (2004)
3. Korbicz, J., Kościelny, J. (eds.): Modeling, Diagnostics and Process Control: Implementation in the DiaSter System. Springer, Berlin (2011)
4. Mrugalski, M., Witczak, M., Korbicz, J.: Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Engineering Applications of Artificial Intelligence 21(6), 895–906 (2008)
5. Mrugalski, M., Witczak, M.: State-space GMDH neural networks for actuator robust fault diagnosis. Advances in Electrical and Computer Engineering 12(3), 65–72 (2012)
6. Kościelny, J., Bartyś, M., Syfert, M.: Method of multiple fault isolation in large scale systems. IEEE Transactions on Control Systems Technology 20(5), 1302–1310 (2012)
7. Noura, H., Theilliol, D., Ponsart, J., Chamseddine, A.: Fault-tolerant Control Systems: Design and Practical Applications. Springer, London (2009)
8. Tong, S., Yang, G., Zhang, W.: Observer-based fault-tolerant control againts sensor failures for fuzzy systems with time delays. International Journal of Applied Mathematics and Computer Science 21(4), 617–628 (2011)
9. Pedro, J., Dahunsi, O.: Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system. International Journal of Applied Mathematics and Computer Science 21, 137–147 (2011)
10. Krzysztof, P., Korbicz, J.: Nonlinear model predictive control of a boiler unit: A fault tolerant control study. International Journal of Applied Mathematics and Computer Science 22(1), 225–237 (2012)
11. Ivakhnenko, A., Mueller, J.: Self-organizing of nets of active neurons. System Analysis Modelling Simulation 20, 93–106 (1995)
12. Mrugalski, M., Korbicz, J., Patton, R.J.: Robust fault detection via GMDH neural networks. In: Proceedings of 16th IFAC World Congress (2005)
13. Korbicz, J., Mrugalski, M.: Confidence estimation of GMDH neural networks and its application in fault detection system. International Journal of System Science 39(8), 783–800 (2008)
14. Mrugalski, M., Korbicz, J.: GMDH neural networks. In: Wilamowski, B., Irwin, J. (eds.) Intelligent systems: The Industrial Electronics Handbook, 2nd edn., vol. 5, pp. 8-1–8-21. CRC Press, Taylor Francis Group, Boca Raton (2011)
15. Mrugalski, M.: An unscented kalman filter in designing dynamic GMDH neural networks for robust fault detection. International Journal of Applied Mathematics and Computer Science 23(1), 157–169 (2013)
16. Witczak, M., Korbicz, J., Mrugalski, M., Patton, R.: A GMDH neural network based approach to robust fault detection and its application to solve the damadics benchmark problem. Control Engineering Practice 14(6), 671–683 (2006)
17. Patan, K., Witczak, M., Korbicz, J.: Towards robustness in neural network based fault diagnosis. International Journal of Applied Mathematics and Computer Science 18(4), 443–454 (2008)
18. Walter, E., Pronzato, L.: Identification of Parametric Models from Experimental Data. Springer, Berlin (1997)
19. Cerone, V., Piga, D., Regruto, D.: Bounded error identification of hammerstein systems through sparse polynomial optimization. Automatica (2012)
20. Mrugalska, B., Kawecka-Endler, A.: Practical application of product design method robust to disturbances. Human Factors and Ergonomics in Manufacturing & Service Industries (2012)
21. Alamo, T., Bravo, J., Camacho, E.: Guaranteed state estimation by zonotopes. Automatica 41(6), 1035–1043 (2005)
22. Bravo, J., Alamo, T., Camacho, E.: Bounded error identification of systems with time-varying parameters. IEEE Transactions on Automatic Control 51(7), 1144–1150 (2006)
23. Mrugalski, M., Witczak, M.: Parameter estimation of dynamic GMDH neural networks with the bounded-error technique. Journal Applied Computer Science 10(1), 77–90 (2002)
24. Mrugalski, M., Arinton, E., Korbicz, J.: Dynamic GMDH type neural networks. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. Advances in Soft Computing, pp. 698–703 (2003)
25. 6th International Conference on Neural Networks and Soft Computing, Zakopane, Poland, June 11-15 (2002)
26. Mrugalski, M., Korbicz, J.: Least mean square vs. Outer bounding ellipsoid algorithm in confidence estimation of the GMDH neural networks. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 19–26. Springer, Heidelberg (2007)
27. Atkinson, A.C., Donev, A.N.: Optimum Experimental Designs. Oxford University Press, New York (1992)