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2008 | 18 | 4 | 443-454
Tytuł artykułu

Towards robustness in neural network based fault diagnosis

Treść / Zawartość
Warianty tytułu
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.
Rocznik
Tom
18
Numer
4
Strony
443-454
Opis fizyczny
Daty
wydano
2008
otrzymano
2008-02-13
poprawiono
2008-06-18
Twórcy
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
Bibliografia
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  • Korbicz J., Patan K. and Kowal M. (Eds.) (2007). Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw.
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  • 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.
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  • 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.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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