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Towards Robustness in Neural Network Based Fault Diagnosis

International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Issues in Fault Diagnosis and Fault Tolerant Control (special issue), Józef Korbicz and Dominique Sauter (Eds.)
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Blanke M., Kinnaert M., Lunze J. and Staroswiecki M. (2003). Diagnosis and Fault-Tolerant Control, Springer, New York, NY.10.1007/978-3-662-05344-7Search in Google Scholar

Chen J. and Patton R. J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer, Berlin.10.1007/978-1-4615-5149-2Search in Google Scholar

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.Search in Google Scholar

Frank P. M. and Köppen-Seliger B. (1997). New developments using AI in fault diagnosis, Engineering Applications of Artificial Intelligence 10(1): 3-14.10.1016/S0952-1976(96)00072-3Search in Google Scholar

Fuessel D. and Isermann R. (2000). Hierarchical motor diagnosis utilising structural knowledge and a self-learning neuro-fuzzy scheme, IEEE Transactions on Industrial Electronics 47(5): 1070-1077.10.1109/41.873215Search in Google Scholar

Gertler J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY.Search in Google Scholar

Haykin S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Ed., Prentice-Hall, Englewood Cliffs, NJ.Search in Google Scholar

Iserman R. (2006). Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Springer, New York, NY.10.1007/3-540-30368-5Search in Google Scholar

Ivakhnenko A. G. and Mueller J. A. (1995). Self-organizing of nets of active neurons, System Analysis Modelling Simulation 20(2): 93-106.Search in Google Scholar

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.Search in Google Scholar

Korbicz J. (2006). Fault detection using analytical and soft computing methods, Bulletin of the Polish Academy of Sciences: Technical Sciences 54(1): 75-88.Search in Google Scholar

Korbicz J., Kościelny J., Kowalczuk Z. and Cholewa W. (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin.10.1007/978-3-642-18615-8Search in Google Scholar

Korbicz J., Patan K. and Kowal M. (Eds.) (2007). Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw.Search in Google Scholar

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): 616-621.10.1784/insi.46.10.616.45210Search in Google Scholar

Ljung L. (1999). System Identification—Theory for the User, Prentice Hall, Englewood Cliffs, NJ.Search in Google Scholar

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.Search in Google Scholar

Milanese M. (2004). Set membership identification of nonlinear systems, Automatica 40(6): 957-975.10.1016/j.automatica.2004.02.002Search in Google Scholar

Milanese M., Norton J., Piet-Lahanier H. and Walter E. (1996). Bounding Approaches to System Identification, Plenum Press, New York, NY.10.1007/978-1-4757-9545-5Search in Google Scholar

Moseler O. and Isermann R. (2000). Application of model-based fault detection to a brushless DC motor, IEEE Transactions on Industrial Electronics 47(5): 1015-1020.10.1109/41.873209Search in Google Scholar

Mrugalski M. (2004). Neural Network Based Modelling of Nonlinear Systems in Fault Detection Schemes., Ph.D. thesis, University of Zielona Góra, (in Polish).Search in Google Scholar

Mueller J. E. and Lemke F. (2000). Self-Organising Data Mining, Libri, Hamburg.Search in Google Scholar

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): 719-729.10.1109/TEC.2005.847955Search in Google Scholar

Narendra K. S. and Parthasarathy K. (1990). Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks 1(1): 12-18.10.1109/72.80202Search in Google Scholar

Nelles O. (2001). Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models, Springer-Verlag, Berlin.10.1007/978-3-662-04323-3Search in Google Scholar

Norgard M., Ravn O., Poulsen N. and Hansen L. (2000). Networks for Modelling and Control of Dynamic Systems, Springer, London.10.1007/978-1-4471-0453-7Search in Google Scholar

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.10.23919/ECC.2007.7068622Search in Google Scholar

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.Search in Google Scholar

Patan K. (2007c). Stability analysis and the stabilization of a class of discrete-time dynamic neural networks, IEEE Transactions on Neural Networks 18(3): 660-673.10.1109/TNN.2007.891199Search in Google Scholar

Patan K. (2008). Aproximation of state-space trajectories by locally recurrent globally feed-forward neural networks, Neural Networks 21(1): 59-64.10.1016/j.neunet.2007.10.004Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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): 67-79.10.1016/j.jprocont.2004.04.001Search in Google Scholar

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): 621-633.10.1016/j.conengprac.2005.03.016Search in Google Scholar

Reinelt W., Garulli A. and Ljung L. (2002). Comparing different approaches to model error modeling in robust identification, Automatica 38(5): 787-803.10.1016/S0005-1098(01)00269-2Search in Google Scholar

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): 27-37.10.2478/v10006-007-0004-5Search in Google Scholar

Rutkowski L. (2004). New Soft Computing Techniques for System Modelling, Pattern Classification and Image Processing, Springer, Berlin.10.1007/978-3-540-40046-2Search in Google Scholar

Walter E. and Pronzato L. (1997). Identification of Parametric Models from Experimental Data, Springer, London.Search in Google Scholar

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.Search in Google Scholar

Witczak M. (2007). Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems, Springer, Berlin.Search in Google Scholar

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): 671-683.10.1016/j.conengprac.2005.04.007Search in Google Scholar

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): 1021-1030.10.1109/41.873210Search in Google Scholar

ISSN:
1641-876X
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Mathematics, Applied Mathematics