Fault diagnosis and isolation of a complex system using a neural network observer
by Rania Loukil; Mohamed Chtourou; Tarak Damak
International Journal of Automation and Control (IJAAC), Vol. 7, No. 3, 2013

Abstract: In this work, we use the approach based on neural observer in order to introduce the diagnosis of a non-linear system. The synthesis of such a trained specific observer using the back-propagation algorithm leads to an estimation study then a determination of fault diagnosis and isolation of single actuator fault based on residual generation. The robustness of the proposed observer is tested through a physical example. Finally, a comparison of observers' performances will be interesting for judging the effectiveness of this approach. So, the obtained results will be compared to the sliding mode observer and the classic Luenberger observer.

Online publication date: Sat, 12-Jul-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Automation and Control (IJAAC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com