Intelligent normalisation for transient classification
by Davide Roverso
International Journal of Nuclear Knowledge Management (IJNKM), Vol. 2, No. 3, 2007

Abstract: A fundamental requirement on data to be used for the training of an empirical diagnostic model is that the data has to be sufficiently similar and consistent with what will be observed during on-line monitoring. In other words, the training data has to ''cover'' the data space within which the monitored process operates. The coverage requirement can quickly become a problem if the process to be monitored has a wide range of operating regimes leading to large variations in the manifestation of the faults of interest in the observable signal transients. In this paper we propose a novel technique, based on neural networks, aimed at reducing the variability of fault manifestations through a process of ''intelligent normalisation'' of transients. The paper includes the application of the proposed method to a nuclear power plant transient classification case study.

Online publication date: Sun, 06-May-2007

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 Nuclear Knowledge Management (IJNKM):
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