Rotating Fault Diagnosis Based on Wavelet Kernel Principal Component

被引：0

作者：

Guo, L. ^{[1
]}

Dong, G. M. ^{[1
]}

Chen, J. ^{[1
]}

Zhu, Y. ^{[1
]}

Pan, Y. N. ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT I, PROCEEDINGS | 2008年 / 5263卷

关键词：

Kernel PCA; wavelet kernel; fault diagnosis; rotating machinery;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the application of nonlinear feature extraction based on wavelet kernel KPCA for faults diagnosis is presented. Mexican hat wavelet kernel is intruded to enhance Kernel-PCA nonlinear mapping capability. The experimental data sets of rotor working under four conditions: normal, oil whirling. rub and unbalance are used to test the WKPCA method. The feature reduction results of WKPCA are compared with that of PCA method and KPCA method. The results indicate that WKPCA can classify the rotor fault type efficiently. The WKPCA is more suitable for nonlinear feature reduction ill fault diagnosis area.

引用

页码：674 / 681

页数：8

共 8 条

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