Korbicz, Józef - ed. ; Sauter, Dominique - ed.
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 18, number 4 (2008) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Apr 8, 2024
Apr 8, 2024
16
https://zbc.uz.zgora.pl/publication/88504
Edition name | Date |
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Towards robustness in neural network based fault diagnosis | Apr 8, 2024 |
Witczak, Marcin Korbicz, Józef - red.
Korbicz, Józef Patan, Krzysztof Obuchowicz, Andrzej Korbicz, Józef - red. Patton, Ronald J. - red.
Marcu, Teodor Mirea, Letitia Frank, Paul M. Korbicz, Józef - red. Patton, Ronald J. - red.
Korbicz, Józef Patan, Krzysztof Korbicz, Józef - red. Uciński, Dariusz - red.
Mrugalski, Marcin Korbicz, Józef - red. Uciński, Dariusz - red.
Laursen, Morten Blanke, Mogens Düştegör, Dilek Korbicz, Józef - ed. Sauter, Dominique - ed.
Kościelny, Jan Maciej Syfert, Michał Rostek, Kornel Sztyber, Anna Korbicz, Józef - red. Uciński, Dariusz - red.
Yasui, Syozo Rutkowska, Danuta - ed. Zadeh, Lotfi A. - ed.