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

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.