Principal Component Analysis Methods for Fault Detection Evaluated on a Nuclear Power Plant Simulation Platform

Yunfeng Zhang, Xiangshun Li, Chuyue Lou, Jin Jiang
{"title":"Principal Component Analysis Methods for Fault Detection Evaluated on a Nuclear Power Plant Simulation Platform","authors":"Yunfeng Zhang, Xiangshun Li, Chuyue Lou, Jin Jiang","doi":"10.1109/ICCSSE.2019.00017","DOIUrl":null,"url":null,"abstract":"In this paper, fault detection methods based on principal component analysis (PCA) have been investigated. The methods have been evaluated using the data acquired from a practical nuclear power plant simulation platform. PCA, dynamic principal component analysis (DPCA), kernel principal component analysis (KPCA), two-step principal component analysis (TS-PCA) have been considered. The performance of these methods in fault detection has been compared.","PeriodicalId":443482,"journal":{"name":"2019 5th International Conference on Control Science and Systems Engineering (ICCSSE)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Control Science and Systems Engineering (ICCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSSE.2019.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this paper, fault detection methods based on principal component analysis (PCA) have been investigated. The methods have been evaluated using the data acquired from a practical nuclear power plant simulation platform. PCA, dynamic principal component analysis (DPCA), kernel principal component analysis (KPCA), two-step principal component analysis (TS-PCA) have been considered. The performance of these methods in fault detection has been compared.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于主成分分析的核电厂仿真平台故障检测方法
本文研究了基于主成分分析(PCA)的故障检测方法。利用实际核电站仿真平台的数据对这些方法进行了评价。分析了主成分分析(PCA)、动态主成分分析(DPCA)、核主成分分析(KPCA)、两步主成分分析(TS-PCA)。比较了这些方法在故障检测中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Icing Aircraft Safety Analysis Based on the Bifurcation Method Station-Keeping Method for GEO All-Electric Propulsion Satellite to Avoid Forbidden Firing Position Development of Teaching Aid for Induction Motor Drive Control System Principal Component Analysis Methods for Fault Detection Evaluated on a Nuclear Power Plant Simulation Platform Design of Semi-Physical Real-Time Simulation System for UAV Based on xPC
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1