%0 Journal Article
%A ZHU Dan1
%A 2
%A FAN Yugang1
%A 2
%A ZOU Jinhui1
%A 2
%A WU Jiande1
%A 2
%A HUANG Guoyong1
%A 2
%T Application of wavelet packet energy spectrum and sparse kernel principal component in fault detection
%D 2014
%R
%J Computer Engineering and Applications
%P 224-229
%V 50
%N 21
%X For the problem of rolling bearing fault detection, a method of rolling bearing fault detection is proposed, which is based on wavelet packet energy spectrum and sparse kernel principal component. The vibration signal is decomposed by wavelet packet, in order to extract the energy spectrum of the signal. Then the sample base of energy spectrum is extracted through the method of incremental sample base. A kernel principal component model is built by the sample base for the analysis of the energy spectrum of the bearing vibration signal. The experimental simulation is presented to illustrate the effectiveness of the algorithm.
%U http://cea.ceaj.org/EN/abstract/article_32545.shtml