Use of principal component analysis for sensor fault identification

被引：45

作者：

Dunia, R

Qin, SJ

Edgar, TF

McAvoy, TJ

机构：

[1] UNIV TEXAS,DEPT CHEM ENGN,AUSTIN,TX 78712

[2] FISHER ROSEMOUNT SYST,AUSTIN,TX 78754

[3] UNIV MARYLAND,DEPT CHEM ENGN,COLLEGE PK,MD 20742

来源：

COMPUTERS & CHEMICAL ENGINEERING | 1996年 / 20卷

关键词：

D O I：

10.1016/0098-1354(96)00128-7

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.

引用

页码：S713 / S718

页数：6

共 7 条

[1]

[Anonymous], 1991, IFAC ADV CONTROLOF C

[2]

DUNIA R, 1996, UNPUB IFAC

[3]

Jackson J. E., 1991, USERS GUIDE PRINCIPL

[4] PRINCIPAL COMPONENTS AND FACTOR-ANALYSIS .1. PRINCIPAL COMPONENTS [J].