Towards robustness in neural network based fault diagnosis

Krzysztof Patan; Marcin Witczak; Józef Korbicz

International Journal of Applied Mathematics and Computer Science (2008)

  • Volume: 18, Issue: 4, page 443-454
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Krzysztof Patan, Marcin Witczak, and Józef Korbicz. "Towards robustness in neural network based fault diagnosis." International Journal of Applied Mathematics and Computer Science 18.4 (2008): 443-454. <http://eudml.org/doc/207898>.

@article{KrzysztofPatan2008,
abstract = {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.},
author = {Krzysztof Patan, Marcin Witczak, Józef Korbicz},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fault diagnosis; robustness; dynamic neural network; GMDH neural network},
language = {eng},
number = {4},
pages = {443-454},
title = {Towards robustness in neural network based fault diagnosis},
url = {http://eudml.org/doc/207898},
volume = {18},
year = {2008},
}

TY - JOUR
AU - Krzysztof Patan
AU - Marcin Witczak
AU - Józef Korbicz
TI - Towards robustness in neural network based fault diagnosis
JO - International Journal of Applied Mathematics and Computer Science
PY - 2008
VL - 18
IS - 4
SP - 443
EP - 454
AB - 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.
LA - eng
KW - fault diagnosis; robustness; dynamic neural network; GMDH neural network
UR - http://eudml.org/doc/207898
ER -

References

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