Title: Fault detection for the Benfield process using a parametric identification approach

Authors: Johannes P. Maree; Ferdinando R. Camisani-Calzolari

Addresses: Department Engineering Cybernetics, Norwegian University of Science and Technology, Institutt for teknisk kybernetikk 7491, Trondheim, Norway ' Opto-Mechatronics Group, Optronic Sensor Systems, Defense, Peace, Safety and Security, CSIR 0001, Pretoria, South Africa

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.

Keywords: closed loop subspace identification; Benfield process; parametric fault detection; process monitoring; simulation.

DOI: 10.1504/IJAAC.2012.048645

International Journal of Automation and Control, 2012 Vol.6 No.2, pp.120 - 139

Received: 07 Dec 2011
Accepted: 02 Jan 2012
Published online: 07 Nov 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article