Statistical χ2 Testing Based Fault Detection for Linear Discrete Time-delay Systems

Q2 Computer Science 自动化学报 Pub Date : 2014-07-01 DOI:10.1016/S1874-1029(14)60013-6
Bo-Ang LIU , Hao YE
{"title":"Statistical χ2 Testing Based Fault Detection for Linear Discrete Time-delay Systems","authors":"Bo-Ang LIU ,&nbsp;Hao YE","doi":"10.1016/S1874-1029(14)60013-6","DOIUrl":null,"url":null,"abstract":"<div><p>This paper is concerned with statistical χ<sup>2</sup> testing based fault detection (FD) for a class of linear discrete time-varying (LDTV) stochastic systems with delayed state. Different from the traditional residual based FD, we propose to construct the evaluation function by directly using measurement observations. Then an equivalent solution can be given in terms of Riccati recursion by utilizing projection and innovation analysis technique. Moreover, the fault free case evaluation function is with central χ<sup>2</sup> distribution and the heavy computational burden is reduced. Furthermore, strategies of χ<sup>2</sup> statistic testing on evaluation function are also discussed. Finally, a numerical example is given to illustrate the proposed method.</p></div>","PeriodicalId":35798,"journal":{"name":"自动化学报","volume":"40 7","pages":"Pages 1278-1284"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1874-1029(14)60013-6","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"自动化学报","FirstCategoryId":"1093","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874102914600136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 3

Abstract

This paper is concerned with statistical χ2 testing based fault detection (FD) for a class of linear discrete time-varying (LDTV) stochastic systems with delayed state. Different from the traditional residual based FD, we propose to construct the evaluation function by directly using measurement observations. Then an equivalent solution can be given in terms of Riccati recursion by utilizing projection and innovation analysis technique. Moreover, the fault free case evaluation function is with central χ2 distribution and the heavy computational burden is reduced. Furthermore, strategies of χ2 statistic testing on evaluation function are also discussed. Finally, a numerical example is given to illustrate the proposed method.

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
基于统计χ2检验的线性离散时滞系统故障检测
研究了一类具有延迟状态的线性离散时变随机系统的基于统计χ2检验的故障检测方法。与传统的基于残差的FD不同,我们提出直接利用测量观测值来构建评价函数。然后利用投影和创新分析技术,用Riccati递推给出了等价解。此外,无故障案例评估函数具有中心χ2分布,减轻了繁重的计算负担。此外,还讨论了评价函数的χ2统计检验策略。最后给出了一个数值算例来说明所提出的方法。
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
自动化学报
自动化学报 Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
4.80
自引率
0.00%
发文量
6655
期刊介绍: ACTA AUTOMATICA SINICA is a joint publication of Chinese Association of Automation and the Institute of Automation, the Chinese Academy of Sciences. The objective is the high quality and rapid publication of the articles, with a strong focus on new trends, original theoretical and experimental research and developments, emerging technology, and industrial standards in automation.
期刊最新文献
Endocrine therapy and urogenital outcomes among women with a breast cancer diagnosis. Robust Approximations to Joint Chance-constrained Problems A Chebyshev-Gauss Pseudospectral Method for Solving Optimal Control Problems Forward Affine Point Set Matching Under Variational Bayesian Framework SAR Image Despeckling by Sparse Reconstruction Based on Shearlets
×
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