Determination of principal component analysis models for sensor fault detection and isolation

被引：28

作者：

Benaicha, Anissa ^{[1
]}

Mourot, Gilles ^{[2
]}

Benothman, Kamel ^{[3
]}

Ragot, Jose ^{[2
]}

机构：

[1] Natl Engn Sch Monastir, Res Unit ATSI, Monastir 5019, Tunisia

[2] Univ Lorraine, CRAN, UMR 7039, CNRS, F-54516 Vandoeuvre Les Nancy, France

[3] Natl Engn Sch Tunis, Res Unit LARA Automat, Tunis 1002, Belvedere, Tunisia

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2013年 / 11卷 / 02期

关键词：

Fault detection and isolation; number of principal components; PCA; sensor fault; variable reconstruction; RECONSTRUCTION; PCA; IDENTIFICATION; SENSITIVITY; NUMBER;

D O I：

10.1007/s12555-012-0142-x

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new method for determining the Principal Component Analysis (PCA) model structure for system diagnosis is proposed. This method, based on the variables reconstruction principle, determines the PCA model optimizing detection and isolation of single or multiple faults affecting redundant or non redundant variables of a system. This new method has been validated by a simulation example.

引用

页码：296 / 305

页数：10

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