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《Journal of Test and Measurement Technology》 2005-02
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Machine Fault Condition Recognition Based on Supervised Kernel PCA

WANG Xin-feng, QIU Jing, LIU Guan-jun(Institute of Mechantronics Engineering, National University of Defense Technology, Changsha 410073, China)  
Abstrcat A Supervised Kernel Principal Component Analysis (SKPCA) is proposed in the paper, which integrates between and within class variances into KPCA, and the classification performances can be enhanced. In gear fault diagnosis example, SKPCA was applied to extract the nonlinear feature from the fault feature set, the result shows contribution of the first and second principle components are more concentrated by SKPCA than by SPCA, and SKPCA has better classification performance than KPCA.
【Fund】: 国防科技行业预研基金资助项目(41319040202)
【CateGory Index】: TH17
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