Towards robustness in neural network based fault diagnosis

• Autorzy: Krzysztof Patan , Marcin Witczak , Józef Korbicz
• Języki publikacji: EN

Abstrakty

EN

Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

Słowa kluczowe

EN

fault diagnosis; robustness; dynamic neural network; GMDH neural network

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 4
• Strony: 443-454

Daty

wydano

2008

otrzymano

2008-02-13

poprawiono

2008-06-18

Twórcy

autor

Krzysztof Patan

• Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland

autor

Marcin Witczak

• Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland

autor

Józef Korbicz

• Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland

Bibliografia

• Blanke M., Kinnaert M., Lunze J. and Staroswiecki M. (2003). Diagnosis and Fault-Tolerant Control, Springer, New York, NY.
• Chen J. and Patton R. J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer, Berlin.
• Duzinkiewicz K. (2006). Set membership estimation of parameters and variables in dynamic networks by recursive algorithms with a moving measurement window, International Journal of Applied Mathematics and Computer Science 16(2): 209-217.
• Frank P. M. and Köppen-Seliger B. (1997). New developments using AI in fault diagnosis, Engineering Applications of Artificial Intelligence 10(1): 3-14.
• Fuessel D. and Isermann R. (2000). Hierarchical motor diagnosis utilising structural knowledge and a self-learning neurofuzzy scheme, IEEE Transactions on Industrial Electronics 47(5): 1070-1077.
• Gertler J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY.
• Haykin S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Ed., Prentice-Hall, Englewood Cliffs, NJ.
• Iserman R. (2006). Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Springer, New York, NY.
• Ivakhnenko A. G. and Mueller J. A. (1995). Self-organizing of nets of active neurons, System Analysis Modelling Simulation 20(2): 93-106.
• Köppen-Seliger B. and Frank P. M. (1999). Fuzzy logic and neural networks in fault detection, in L. Jain and N. Martin (Eds.), Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms, CRC Press, New York, NY, pp. 169-209.
• Korbicz J. (2006). Fault detection using analytical and soft computing methods, Bulletin of the Polish Academy of Sciences: Technical Sciences 54(1): 75-88.
• Korbicz J., Kościelny J., Kowalczuk Z. and Cholewa W. (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin.
• Korbicz J., Patan K. and Kowal M. (Eds.) (2007). Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw.
• Li L., Mechefske C. K. and Li W. (2004). Electric motor faults diagnosis using artificial neural networks, Insight: Non-Destructive Testing and Condition Monitoring 46(10): 616-621.
• Ljung L. (1999). System Identification-Theory for the User, Prentice Hall, Englewood Cliffs, NJ.
• Marcu T., Mirea L. and Frank P. M. (1999). 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.
• Milanese M. (2004). Set membership identification of nonlinear systems, Automatica 40(6): 957-975.
• Milanese M., Norton J., Piet-Lahanier H. and Walter E. (1996). Bounding Approaches to System Identification, Plenum Press, New York, NY.
• Moseler O. and Isermann R. (2000). Application of model-based fault detection to a brushless DC motor, IEEE Transactions on Industrial Electronics 47(5): 1015-1020.
• Mrugalski M. (2004). Neural Network Based Modelling of Nonlinear Systems in Fault Detection Schemes., Ph.D. thesis, University of Zielona Góra, (in Polish).
• Mueller J. E. and Lemke F. (2000). Self-Organising Data Mining, Libri, Hamburg.
• Nandi S., Toliyat H. A. and Li X. (2005). Condition monitoring and fault diagnosis of electrical motors-A review, IEEE Transactions on Energy Conversion 20(4): 719-729.
• Narendra K. S. and Parthasarathy K. (1990). Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks 1(1): 12-18.
• Nelles O. (2001). Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models, Springer-Verlag, Berlin.
• Norgard M., Ravn O., Poulsen N. and Hansen L. (2000). Networks for Modelling and Control of Dynamic Systems, Springer, London.
• Patan K. (2007a). Approximation ability of a class of locally recurrent globally feed-forward neural networks, Proceedings of the European Control Conference, ECC 2007, Kos, Greece, published on CD-ROM.
• Patan K. (2007b). Robust faul diagnosis in a DC motor by means of artificial neural networks and model error modelling, in J. Korbicz, K. Patan and M. Kowal (Eds.), Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw, pp. 337-346.
• Patan K. (2007c). Stability analysis and the stabilization of a class of discrete-time dynamic neural networks, IEEE Transactions on Neural Networks 18(3): 660-673.
• Patan K. (2008). Aproximation of state-space trajectories by locally recurrent globally feed-forward neural networks, Neural Networks 21(1): 59-64.
• Patan K., Korbicz J. and Głowacki G. (2007). DC motor fault diagnosis by means of artificial neural networks, Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007, Angers, France, published on CD-ROM.
• Patan K. and Parisini T. (2002). Stochastic learning methods for dynamic neural networks: Simulated and real-data comparisons, Proceedings of the 2002 American Control Conference, ACC'02, Anchorage, AK, USA, pp. 2577-2582.
• Patan K. and Parisini T. (2005). 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.
• Puig V., Stancu A., Escobet T., Nejjari F., Quevedo J. and Patton, R. J. (2006). Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem, Control Engineering Practice 14(6): 621-633.
• Reinelt W., Garulli A. and Ljung L. (2002). Comparing different approaches to model error modeling in robust identification, Automatica 38(5): 787-803.
• Rodrigues M., Theilliol D., Aberkane S. and Sauter D. (2007). Fault tolerant control design for polytopic LPV systems, International Journal of Applied Mathematics and Computer Science 17(1): 27-37.
• Rutkowski L. (2004). New Soft Computing Techniques for System Modelling, Pattern Classification and Image Processing, Springer, Berlin.
• Walter E. and Pronzato L. (1997). Identification of Parametric Models from Experimental Data, Springer, London.
• Witczak M. (2006). Advances in model-based fault diagnosis with evolutionary algorithms and neural networks, International Journal of Applied Mathematics and Computer Science 16(1): 85-99.
• Witczak M. (2007). Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems, Springer, Berlin.
• Witczak M., Korbicz J., Mrugalski M. and Patton R. J. (2006). A GMDH neural network-based approach to robust fault diagnosis: Application to the DAMADICS benchmark problem, Control Engineering Practice 14(6): 671-683.
• Xiang-Qun L. and Zhang H. Y. (2000). Fault detection and diagnosis of permanent-magnet DC motor based on parameter estimation and neural network, IEEE Transactions on Industrial Electronics 47(5): 1021-1030.

Identyfikatory

DOI

10.2478/v10006-008-0039-2