By Topic

Improved diagnosis of sensor faults using multivariate statistics

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
$33 $13
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)
Lieftucht, D. ; Intelligent Syst. & Control Res. Group, Queen''s Univ., Belfast, UK ; Kruger, U. ; Irwin, G.W.

This paper analyses a variable reconstruction technique for identifying a faulty sensor. The reconstruction is associated with the application of principal component analysis (PCA) and attempts to remove "fault information" from the sensor reading. It is shown that the reconstruction (i) affects the geometry of the PCA decomposition (ii) leads to changes in the covariance matrix of the sensor readings and (iii) alters the determination of PCA based monitoring statistics in terms of their confidence limits. These changes must be incorporated into the monitoring scheme, as false alarms may otherwise be encountered. Consequently, an improved reconstruction based fault diagnosis is proposed here.

Published in:

American Control Conference, 2004. Proceedings of the 2004  (Volume:5 )

Date of Conference:

June 30 2004-July 2 2004