State-space GMDH neural networks for actuator robust fault diagnosis

Advances in Electrical and Computer Engineering

Volumn 12, Issue 3, 2012, Pages 65-72

  Mrugalski, Marcin ^a   Witczak, Marcin ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Actuators; Fault diagnosis; Neural networks; Robustness; System identification

Indexed keywords

EID: 84865856659     PISSN: 15827445     EISSN: 18447600     Source Type: Journal    
DOI: 10.4316/AECE.2012.03010     Document Type: Article

References (31)

1. R. Aarenstrup, "DC motor model," June 2012. [Online]. Available: http://www.mathworks.com/matlabcentral/fileexchange/11829-dc-motor-model
2. D. Berdjag, V. Cocquempot, C. Christophe, A. Shumsky, and A. Zhirabok, "Algebraic approach for model decomposition: application to fault detetion and isolation in discrete-event systems," International Journal of Applied Mathematics and Computer Science, vol. 21, pp. 109-125, 2011.
3. M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault-Tolerant Control. Berlin, Heidelberg, New York: Springer-Verlag, 2003.
4. S. De Oca, V. Puig, M. Witczak, and L. Dziekan, "Fault-tolerant control strategy for actuator faults using lpv techniques: Application to a two degree of freedom helicopter," International Journal of Applied Mathematics and Computer Science, vol. 22, no. 1, pp. 161-171, 2012.
5. S. Ding, Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Berlin/Heidelberg: Springer-Verlag, 2008.
6. S. Gillijns and B. D. Moor, "Unbiased minimum-variance input and state estimation for linear discrete-time systems," Automatica, vol. 43, pp. 111-116, 2007.
7. S. Haykin, Kalman Filtering and Neural Networks. New York: John Wiley & Sons, 2001.
8. S. Haykin, Neural Networks and Learning Machines. New York: Prentice Hall, 2009.
9. R. Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Heidelberg/Berlin: Springer-Verlag, 2005.
10. A. Ivakhnenko and J. Mueller, "Self-organization of nets of active neurons," System Analysis Modelling Simulation, vol. 20, pp. 93-106, 1995.
11. S. Julier and J. Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
12. T. Kondo and J. Ueno, "Nonlinear system identification by feedback gmdh-type neural network with architecture self-selecting function," in Intelligent Control (ISIC), 2010 IEEE International Symposium on, sept. 2010, pp. 1521-1526.
13. J. Korbicz and M. Mrugalski, "Confidence estimation of gmdh neural networks and its application in fault detection system," International Journal of System Science, vol. 39, no. 8, pp. 783-800, 2008.
14. J. Korbicz, M. Witczak, and V. Puig, "Lmi-based strategies for designing observers and unknown input observers for non-linear discrete-time systems," Technical Sciences, vol. 55, no. 1, pp. 31-42.
15. L. Kral and M. Simandl, "Functional adaptive controller for multivariable stochastic systems with dynamic structure of neural network," Adaptive Control and Signal Processing, vol. 25, pp. 949-964, 2011.
16. T. Lee and Z. Jiang, "On uniform global asymptotic stability of nonlinear discrete-time systems with applications," IEEE Trans. Automatic Control, vol. 51, no. 10, pp. 1644-1660, 2006.
17. L. Ljung, System Identification: Theory for the User. Upper Saddle River, New York: Prentice Hall PTR, 1999.
18. M. Mrugalski and J. Korbicz, "Gmdh neural networks," in The Industrial Electronics Handbook, 2nd ed., B. Wilamowski and J. Irwin, Eds. Boca Raton: CRC Press, Taylor Francis Group, 2011, vol. 5, pp. 8-1-8-21.
19. M. Mrugalski, J. Korbicz, and R. Patton, "Robust fault detection via gmdh neural networks," Proceedings of 16th IFAC World Congress, IFAC, 2005.
20. M. Mrugalski and M. Witczak, "Parameter estimation of dynamic gmdh neural networks with the bounded-error technique," J. Appl. Comput. Sci, vol. 10, no. 1, pp. 77-90, 2002.
21. 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.
22. H. Niemann, "A model-based approach to fault-tolerant control," International Journal of Applied Mathematics and Computer Science, vol. 22, no. 1, pp. 67-86, 2012.
23. H. Noura, D. Theilliol, J. Ponsart, and A. Chamseddine, Fault-tolerant Control Systems: Design and Practical Applications. London: Springer-Verlag, 2009.
24. R. Patton, P. Frank, and R. Clark, Non-linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Berlin: Springer-Verlag, 2000.
25. I. Peddle, "Discrete state space control," Control Systems, vol. 414, pp. 2-3, 2007.
26. T. Senguler and E. K. amd S. Seker, "A new mlp approach for the detection of the incipient bearing damage," Advances in Electrical and Computer Engineering, vol. 10, no. 3, pp. 34-39, 2010. [Online]. Available: http://dx.doi.org/10.4316/AECE.2010.03006
27. O. Straka, J. Dunik, and M. Simandl, "Truncation nonlinear filters for state estimation with nonlinear inequality constraints," Automatica, vol. 48, pp. 273-286, 2012.
28. B. Teixeira, L. Torres, L. Aguirre, and D. Bernstein, "On unscented kalman filtering with state interval constraints," Journal of Process Control, vol. 20, no. 1, pp. 45-57, 2010.
29. M. Witczak, Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems. From Analytical to Soft Computing Approaches. Berlin: Springer-Verlag, 2007.
30. M. Witczak, J. Korbicz, M. Mrugalski, and R. Patton, "A gmdh neural network based approach to robust fault detection and its application to solve the damadics benchmark problem," Control Engineering Practice, vol. 14, no. 6, pp. 671-683, 2006.
31. M. Witczak and P. Pretki, "Design of an extended unknown input observer with stochastic robustness techniques and evolutionary algorithms," International Journal of Control, vol. 80, no. 5, pp. 749-762, 2007.