By Topic

Fault diagnosis method based on the EWMA dynamic kernel principal component analysis

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)
Shu-kai Qin ; Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang ; Xue-peng Fu ; Xiao-Bo Chen

As widely used method for multivariate statistical process monitoring and fault diagnosis, the conventional principal component analysis (PCA) method is limited to the application of linear and time-invariant systems, and it canpsilat handle the sequence related question of the data. To handle the nonlinear and time-varying characteristics of the real processes, and the sequence related question of the data, a new monitoring and fault diagnosis method based on the EWMA dynamic kernel PCA (EKPCA) for nonlinear process is proposed in this paper. The simulation results for monitoring and fault diagnosis of three water tank system show the effectiveness of this method.

Published in:

Control and Decision Conference, 2008. CCDC 2008. Chinese

Date of Conference:

2-4 July 2008