Improved sparse representation based on local preserving projection for the fault diagnosis of multivariable system

被引：0

作者：

Qiu TANG ^{[1
,2
]}

Benqi LI ^{[3
]}

Yi CHAI ^{[1
,2
]}

Jianfeng QU ^{[1
,2
]}

Hao REN ^{[1
,2
]}

机构：

[1] College of Automation,Chongqing University

[2] Key Laboratory of Complex System Safety and Control,Ministry of Education

[3] Xichang Satellite Launch Center

来源：

Science China(Information Sciences) | 2021年 / 64卷 / 02期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP277 [监视、报警、故障诊断系统];

学科分类号：

0804 ; 080401 ; 080402 ;

摘要：

Dear editor,To satisfy the increasing requirements for safety and quality in industrial processes, process monitoring has been actively investigated in the past decade. The most critical aspects of this approach are the detection of faults in realtime and the diagnosis of fault types. Redundancy and coupling among these variables make it difficult to identify existing correlations between faults and variables, which hinders the quick detection of faults. Furthermore, the large amount of available monitoring data often obscures information about abnormalities and faults. Effective dimension reduction and feature extraction are imperative to address this challenge [1, 2].

引用

页码：258 / 260

页数：3