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.
@article{bwmeta1.element.bwnjournal-article-amcv18i4p443bwm,
author = {Krzysztof Patan and Marcin Witczak and J\'ozef Korbicz},
title = {Towards robustness in neural network based fault diagnosis},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {18},
year = {2008},
pages = {443-454},
zbl = {1155.93344},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv18i4p443bwm}
}
Krzysztof Patan; Marcin Witczak; Józef Korbicz. Towards robustness in neural network based fault diagnosis. International Journal of Applied Mathematics and Computer Science, Tome 18 (2008) pp. 443-454. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv18i4p443bwm/
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