Robust fault detection using analytical and sort computing methods

Korbicz, J.

Artykuł - szczegóły

Tytuł artykułu

Robust fault detection using analytical and sort computing methods

Autorzy

Korbicz J.

Wybrane pełne teksty z tego czasopisma

http://journals.pan.pl/dlibra/journal/95347

Identyfikatory

Warianty tytułu

Języki publikacji

Abstrakty

The paper focuses on the problem of robust fault detection using analytical methods and soft computing. Taking into account the model-based approach to Fault Detection and Isolation (FDI), possible applications of analytical models, and first of all observers with unknown inputs, are considered. The main objective is to show how to employ the bounded-error approach to determine the uncertainty of soft computing models (neural networks and neuro-fuzzy networks). It is shown that based on soft computing models uncertainty defined as a confidence range for (he model output, adaptive thresholds can be described. The paper contains a numerical example that illustrates the effectiveness of the proposed approach for increasing the reliability of fault detection. A comprehensive simulation study regarding the DAMADICS benchmark problem is performed in the final part.

Słowa kluczowe

fault detection robustness unknown input observer neural networks neuro-fuzzy system bounded-error approach model uncertainty

Wydawca

Polska Akademia Nauk, Wydział IV Nauk Technicznych

Czasopismo

Bulletin of the Polish Academy of Sciences. Technical Sciences

Rocznik

2006

Tom

Vol. 54, nr 1

Strony

75--88

Opis fizyczny

Bibliogr. 67 poz., 18 rys., 2 tab.

Twórcy

autor

Korbicz J.

Institute of Control and Computation Engineering, University of Zielona Góra, 50 Podgórna St., 65-246 Zielona Góra, Poland, j.korbicz@issi.uz.zgora.pl

Bibliografia

[1] M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault-Tolerant Control, New York: Springer-Verlag, 2003
[2] J. Gertler, Fault Detection and Diagnosis in Engineering Systems, New York: Marcel Dekker, Inc., 1998
[3] J. Korbicz, "Advances in fault diagnosis systems", Proc. 10th IEEE Int. Conf. on Methods and Models in Automation and Robotics MMAR, 725-733 (2004).
[4] J. Korbicz, J.M. Kościelny, Z. Kowalczuk, and W. Cholewa (eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications. Berlin: Springer-Verlag, 2004.
[5] R.J. Patron, P.M. Frank, and R.N. Clark (eds.), Issues of Fault Diagnosis for Dynamic Systems, Berlin: Springer-Verlag, 2000.
[6] J.M. Kościelny, Diagnostics of Automatized Industrial Processes, Warsaw: Akademicka Oficyna Wydawnicza, EXIT, 2001, (in Polish).
[7] J. Chen and R.J. Patron, Robust Model-Based Fault Diagnosis for Dynamic Systems, London: Kluwer Academic Publishers, 1999.
[8] P.M. Frank, "Handling modelling uncertainty in fault detection and isolation systems", Proc. 9th Int. Conf. IPMU, Annecy, France, 1729-1749 (2002).
[9] M. Witczak, Identification and Fault Detection of Non-linear Dynamic Systems, University of Zielona Góra Press, 2003.
[10] P.M. Frank, G. Schreier, and E.A. Garcia, "Nonlinear observers for fault detection and isolation", in: New Directions in Nonlinear Observer Design (Nijmeijer H., Fossen TJ., eds.), Berlin: Springer-Verlag, 1999.
[11] M. Witczak, A. Obuchowicz, and J. Korbicz, "Genetic programming based approaches to identification and fault diagnosis of nonlinear dynamic systems", Int. J. Control, 75 (13), 1012-1031 (2002).
[12] P.M. Frank, "Enhancement of robustness in observer-based fault detection", Int. J. Control 59, 955-981 (1994).
[13] J. Korbicz and C. Cempel (eds.), "Analytical and knowledge based redundancy in fault detection and diagnosis", Appl. Math. and Comp. Sci., Spec. Issue 3 (3), (1993).
[14] J.M.F. Calado, J. Korbicz, K. Patan, R.J. Patron, and J.M.G. Sada Costa, "Soft computing approaches to fault diagnosis for dynamic systems", European Journal of Control 7 (2-3), 248-286 (2001).
[15] R.J. Patron and J. Korbicz (eds.), "Advances in computational intelligence for fault diagnosis systems", Special Issue of Int. J. Appl. Math. and Comp. Sci. 9 (3), (1999).
[16] J. Korbicz, A. Obuchowicz, and D. Uciński, Artificial Neural Networks. Foundation and Applications, Warsaw: Akademicka Oficyna Wydawnicza, PU, 1994, (in Polish).
[17] J. Korbicz and M. Kowal, "Neuro-fuzzy systems in process diagnosis", in: Fuzzy Sets and Their Applications (J Chojcan, J. Łęski, eds.), Gliwice: Silesian Technical University Press, 2001, (in Polish).
[18] L. Rutkowski, Flexible Neuro-Fuzzy Systems. Structures, Learning and Performance Evaluation, Boston: Kluwer Academic Publishers, 2004.
[19] M. Basseville and L.V. Nikiforov, Detection of Abrupt Changes, Theory and Appliction, London: Prentice Hall, 1993.
[20] M. Milanese, J. Norton, H. Piet-Lahanier, and E. Walter (eds.), Bounding Approaches to System Identification, New York: Plenum Press, 1996.
[21] E. Walter and L. Pronzato, Identification of Parametric Models from Experimental Data, Berlin: Springer-Verlag, 1997.
[22] J. Korbicz, J.M. Kościelny, Z. Kowalczuk, and W. Cholewa (eds.), Process Diagnosis. Models, Methods of Artificial Intelligence, Applications, Warsaw: WNT, 2002, (in Polish).
[23] R.J. Patron and J. Chen, "Observer-based fault detection and isolation: Robustness and applications", Control Eng. Practice 5, 671-682 (1997).
[24] B.D.O. Anderson and J.B. Moore, Optimal Filtering, New Jersey: Prentice Hall, 1979.
[25] J. Korbicz and P. Bidyuk, State and Parameter Estimation. Digital and Optimal Filtering. Applications, Zielona Góra: Technical University Press, 1993.
[26] Z. Kowa1czuk and P. Suchomski, "Optimal detection observers based on eigenstructure assignment", in: Fault Diagnosis. Models, Artificial Intelligence, Applications (Korbicz J., Kościelny lM., Kowlaczuk Z., Cholewa W, eds.), Berlin: Springer-Verlag, 219-260, (2004).
[27] J. Korbicz, Z. Fathi, and W.F. Ramirez, "State estimation schemes for fault detection and diagnosis in dynamic systems", Int. J. Systems Sci. 24 (5), 985-1000 (1993).
[28] M. Boutayeb and D. Aubry, " A strong tracking extended Kalman observer for non-linear discrete time systems", IEEE Trans. Automat. Control 44, 1550--1556 (1999).
[29] A. Edelmayer, J. Bokor, Z. Szabo, and F. Szigeti, "Input reconstruction by means of system inversion. A geometric approach to fault detection and isolation in nonlinear systems", Int. J. Appl. Math. and Comp. Sci. 14 (2),189-199 (2004).
[30] J.R. Koza, Genetic Programming. On the Programming of Computers by Means of Natural Selection, Cambridge: MIT Press, 1992.
[31] M.F. Metenidis, M. Witczak, and J. Korbicz, "A novel genetic programming approach to nonlinear system modelling. Application to the DAMADICS benchmark problem", Eng. Appl. of Artificial Intelligence 17, 363-370 (2004).
[32] R. Isermann (ed.), "Supervision, fault detection and diagnosis of technical systems", Control Eng. Practice, Special Section 5(5), (1997).
[33] D. Uciński, Optimal Measurements Methods for Distributed Parameter System Identification, New York: CRC Press, 2005.
[34] P.M. Frank and T. Marcu, "Diagnosis strategies and systems. Principles, fuzzy and neural approaches", in: Intelligent Sys tems and Interfaces (H.-N. Teodorescu, D. Mlynek, A. Kandel, and H.-J. Zimmermann, eds.), Boston: Kulwer, 2000.
[35] A. Obuchowicz, Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis, Zielona Góra: Lubuskie Scientific Society, 2003.
[36] M. Brown and C.J. Harris, Neuro-Fuzzy Adaptive Modelling and Control, London: Prentice Hall, 1994.
[37] D. Rutkowska, Neuro-fuzzy Architectures and Hybrid Learning, Heidelberg: Physica- Verlag, 2000.
[38] RJ. Patton, J. Korbicz, M. Witczak and F. Uppal, "Combined computational intelligence and analytical methods in fault diagnosis", in: Intelligent Control Systems, A.E. Ruano (ed.), London: IEE Press, pp. 349-392, 2005.
[39] W. Duch, J. Korbicz, L Rutkowski, and R. Tadeusiewicz (eds.), Biocybernetics and Biomedical Engineering 2000. Neural Networks, Vol. 6, Akademicka Oficyna Wydawnicza, EXIT, Warsaw 2000, (in Polish).
[40] M.M. Gupta, L. Jin, and N. Homma, Static and Dynamic Neural Networks, New Jersey: John Wiley & Sons, 2003.
[41] S. Haykin, Neural Networks. A Comprehensive Foundation, Upper Saddle River: Prientice-Hall, 1999.
[42] S.J. Farlow (ed.), Self-Organizing Methods in Modeling GMDH Type Algorithms, New York: Marcel Dekker, 1984.
[43] A.G. Ivakhnenko, "Polynominal theory of complex systems", IEEE Trans. System, Man and Cybernetics 1 (4), 44-58 (1971).
[44] D.T. Pham and L. Xing, Neural Networks for Identification, Prediction and Control, London: Springer-Verlag, 1995.
[45] M. Mrugalski, Neural Network Based Modelling of Non-linear Systems in Fault Detection Schemes, University of Zielona Góra, Faculty of Electrical Engineering, Computer Science and Telecommunications, doctoral dissertation, 2004, (in Polish).
[46] J.E. Mueller and F. Lemke, Self-organising Data Maining, Hamburg: Libri, 2000.
[47] L. Ljung, System Identifivation. Theory for the Users, New Jersey: Prentice-Hall, 1987.
[48] G.A.F. Seber, Nonlinear Regression, New York: John Wiley & Sons, 1989.
[49] S.H. Mo and J.P. Norton, "Fast and robust algorithm to compute exact polytope parameter bounds", Math. and Comp. in Simulation 32, 481-493, (1990).
[50] M. Witczak, J. Korbicz, M. Mrugalski and R.J. Patton, "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).
[51] J. Korbicz, M.F. Metendis, M. Mrugalski, and M. Witczak, "Confidence estimation of GMDH neural networks", in: Artificial Intelligence and Soft Computing, ICAISC, Lecture Notes in Artificial Intelligence, L. Rutkowski, J. Siekman, R. Tadeusiewicz, L.A. Zadeh (eds.), 3070, 210--216, (2004).
[52] J.M.F. Calado and J.M.G. Sá da Costa, "An expert system coupled with a hierarchical structure of fuzzy neural networks for fault diagnosis", Appl. Math. and Comput. Science 9 (3), 667-688, (1999).
[53] S-J.,C-H. Woo, H-S. Hwang, and K.B. Woo, "Evolutionary design of fuzzy role base for nonlinear system modeling and control", IEEE Trans. of Fuzzy Systems 8 (I), 37-45 (2000).
[54] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programmes, Berlin: Springer- Verlag, 1996.
[55] S. Chili, "Fuzzy model identification based on cluster estimation", Journal of Intelligent and Fuzzy Systems 2 (3), 665-685 (1994).
[56] O. Nelles, Nonlinear System Identification, Berlin: Springer Verlag, 2001.
[57] M. Kowal, Optimization of Neura-Fuzzy Structures in Technical Diagnostics Systems, University of Zielona Góra, Faculty of Electrical Engineering, Computer Science and Telecommunications, doctoral dissertation, 2004, (in Polish).
[58] M. Kowal, J. Korbicz, M.J.G.C. Mendes, and J.M.F. Calado, "Fault detection using neuro-fuzzy networks", Systems Science 28 (I), 45-57 (2002).
[59] R. Babuska, Fuzzy Modelling in Contral, London: Kluwer Academic Publishers, 1998.
[60] A. Piegat, Fuzzy Modelling and Control, Berlin: Springer Verlag, 2001.
[61] M. Kowal and J. Korbicz, "Robust fault detection using neurofuzzy networks", Proc. 16th IFAC World Congress, Prague, Czech Republic (2005) (CD ROM).
[62] L. Rutkowski, New Soft Computing Techniques for System Modelling, Pattern Classification and Image Processing, Berlin: Springer- Verlag, 2004.
[63] DAMADICS, Website of the Reseach Training Network on the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, http://diag.mchtr.pw.edu.pl/damadics
[64] J.M. Kościelny and M. Bartyś, "Application of information system theory for actuator diagnosis", Proc. 4th IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS, Budapest 2, 949-954 (2000).
[65] J.C. Yang and D.W Clarke, "The self-validating actuator", Contr. Eng. Practice 7, 249-260 (1999).
[66] M. Mrugalski, J. Korbicz, and R.J. Patton, “Robust fault detection via GMDH neutral networks”, Proc. IFAC World Congress, Prague, Czech Republic, (CD ROM)
[67] Z. Bubnicki, Analysis and Decision Making in Uncertain Systems, London: Springer-Verlag, 2004.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-article-BPG5-0012-0076