Improved kernel fisher discriminant analysis for fault diagnosis

被引：46

作者：

Li, Junhong ^{[2
]}

Cui, Peiling ^{[1
]}

机构：

[1] Beijing Univ Aeronaut & Astronaut, Sch Instrumentat Sci & Optoelect Engn, Beijing 100083, Peoples R China

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

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Fault diagnosis; Kernel fisher discriminant analysis (KFDA); Feature vector selection (FVS); Nearest Feature line (NFL); PRINCIPAL COMPONENT; IDENTIFICATION;

D O I：

10.1016/j.eswa.2007.11.043

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 fisher discriminant analysis (KFDA) for fault diagnosis from three aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KFDA when the number of samples becomes large. Secondly, it ne v kernel function, called the Cosine kernel, is proposed to increase the discriminating capability of the original polynomial kernel function. Thirdly, nearest feature line (NFL) classifier is employed to further enhance the fault diagnosis performance when the sample number is very small. Experimental results show the effectiveness of our methods. (c) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1423 / 1432

页数：10

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