Fault detection for the Benfield process using a parametric identification approach
by Johannes P. Maree; Ferdinando R. Camisani-Calzolari
International Journal of Automation and Control (IJAAC), Vol. 6, No. 2, 2012

Abstract: A closed-loop process monitoring framework that entails subspace identification, and parametric fault detection are discussed, and subsequently applied to the Benfield process for fault detection. An extension to the derivation of the observability matrix of the subspace identification method guarantees stable identified system matrices. For fault detection, extended Kalman filtering is utilized to recursively update a joint-set of initial system states and parameters, using current sampled process data and initial estimated parameters, obtained via the subspace method. Framework validation and verification is established via simulation, as well as using delayed, real-time measured process data from the Benfield process.

Online publication date: Fri, 07-Nov-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