<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Towards Robustness in Neural Network Based Fault Diagnosis

Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Towards Robustness in Neural Network Based Fault Diagnosis"}

Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable...

Towards Robustness in Neural Network Based Fault Diagnosis

Online veröffentlicht: 30. Dez. 2008

Seitenbereich: 443 - 454

DOI: https://doi.org/10.2478/v10006-008-0039-2

Schlüsselwörter
fault diagnosis, robustness, dynamic neural network, GMDH neural network

This content is open access.

Towards Robustness in Neural Network Based Fault Diagnosis

Online veröffentlicht: 30. Dez. 2008

Seitenbereich: 443 - 454

DOI: https://doi.org/10.2478/v10006-008-0039-2

Schlüsselwörterfault diagnosis, robustness, dynamic neural network, GMDH neural network

This content is open access.

Schlüsselwörter
fault diagnosis, robustness, dynamic neural network, GMDH neural network