By Topic

Fault Feature Extraction Based on Kernel Principal Component Analysis for Helicopter Rotor

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

3 Author(s)
Hongmei Liu ; Sch. of Reliability & Syst. Eng., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China ; Chen Lu ; Shaoping Wang

Considering difficulty in choice of fault feature and deficiency of principal component analysis for helicopter rotor, an effective fault feature choice method based on kernel principal component analysis is presented and realized. A nonlinear mapping from original feature space into high dimensional feature space is realized by calculating inner product kernel function in original feature space. And nonlinear principal components of original feature data are obtained through principal component analysis of mapped data in high dimensional feature space. Experiment result indicated that kernel principal component analysis can not only decrease the dimension of feature vector space, but also decrease the complexity of fault classifier and increase the precision of classification.

Published in:

Pattern Recognition (CCPR), 2010 Chinese Conference on

Date of Conference:

21-23 Oct. 2010