By Topic

Improvement of the sensitivity of T2 quality control charts by variable grouping and dimension reducing

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 $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

2 Author(s)
Friebel, T. ; Dept. of Process Eng. & Plant Design, Cologne Univ. of Appl. Sci., Köln, Germany ; Haber, R.

With increasing number of variables the Hotelling's T2 statistic can detect only larger failures in the variables. A new method is introduced for reducing the dimension of the Hotelling's statistic in order to detect smaller failures. The basic idea is to group some variables into a combined variable and to calculate the T2 value from this variable and from the remaining variables. As the new calculated variable has not a Gaussian distribution a proper static transformation is applied. Both uncorrelated and correlated data are dealt with. In the latter case principal component analysis is used before calculating T2. Several simulations show that the sensitivity of the new T2 control charts is improved. The theory is confirmed by an application of sensor fault monitoring of a gas analyzer.

Published in:

Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on

Date of Conference:

12-15 Dec. 2011