Improved kernel principal component analysis for fault detection

被引：82

作者：

Cui, Peiling ^{[1
,2
]}

Li, Junhong ^{[3
]}

Wang, Guizeng ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Beijing Univ Aeronaut & Astronaut, Sch Instrumentat Sci & Opto Elect Engn, Beijing 100083, Peoples R China

[3] Aigo Res Inst Image Comp, Beijing 100089, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2008年 / 34卷 / 02期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

kernel principal component analysis (KPCA); feature vector selection (FVS); Fisher discriminant analysis (FDA); fault detection;

D O I：

10.1016/j.eswa.2006.12.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper improves kernel principal component analysis (KPCA) for fault detection from two aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KPCA when the number of samples becomes large. Secondly, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of these improvements for fault detection performance in terms of low computational cost and high fault detection rate. (C) 2006 Elsevier Ltd. All rights reserved.

引用

页码：1210 / 1219

页数：10

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