期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 34, 期 2, 页码 1210-1219出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2006.12.010
关键词
kernel principal component analysis (KPCA); feature vector selection (FVS); Fisher discriminant analysis (FDA); fault detection
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据