Cart (Loading....) | Create Account
Close category search window

Fault detection in an overheads condenser using multivariate SPC

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wilson, D.J.H. ; Dept. of Electr. & Electron. Eng., Queen''s Univ., Belfast, UK ; Irwin, G.W. ; Lightbody, G.

Fault detection is a part of every industrial engineer's brief, particularly in chemical plants. Traditional detection methods in this field have depended on limit checking of measurable output variables using standard SPC techniques; however, this approach is fraught with problems, notably: alarms are not raised until the fault is actually manifesting itself at the outputs; noise on the outputs may mask incipient faults; and many plants have too many variables to monitor them all. More recent fault detection schemes have tended to be model-based, using various types of model. The major problem is model identification, particularly in large plants with input and output variables. The approach described in this paper uses a combination of these two procedures. A statistical model is generated via partial least squares (PLS), a multivariate statistical modelling technique. Results from simulation studies on an EPSRC-funded benchmark plant, consisting of an overheads condenser and a reflux drum, are presented to illustrate the success of the approach. Standard SPC techniques are then used to detect simulated faults by analysis of the mismatch between the PLS model prediction and the original plant.

Published in:

Control '96, UKACC International Conference on (Conf. Publ. No. 427)  (Volume:1 )

Date of Conference:

2-5 Sept. 1996

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.