Robust statistical process monitoring

被引:12
作者
Chen, J [1 ]
Bandoni, A [1 ]
Romagnoli, JA [1 ]
机构
[1] UNIV SYDNEY,DEPT CHEM ENGN,ICI,LAB PROC SYST ENGN,SYDNEY,NSW 2006,AUSTRALIA
关键词
D O I
10.1016/0098-1354(96)00092-0
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Principal component analysis (PCA) is a key step to carrying out multivariate statistical process monitoring. Due to the sensitive nature of classical PCA. one or two outliers will cause misleading results. In this paper. a robust PCA via a Hybrid Projection Pursuit (HPP) approach is proposed. Incorporation of this robust PCA into our previously developed data driven strategy. for statistical process monitoring. will mean the whole procedure will be resistant to outliers and this robust. The performance of the proposed approach is demonstrated by simulation studies on a simple flowsheet example.
引用
收藏
页码:S497 / S502
页数:6
相关论文
共 16 条
[1]  
BAHRI PA, 1995, IN PRESS AICHE J
[2]  
CHEN J, 1995, UNPUB CHEM ENG COMPU
[3]  
CHEN J, 1995, UNPUB AICHE J
[4]   ROBUST ESTIMATION OF DISPERSION MATRICES AND PRINCIPAL COMPONENTS [J].
DEVLIN, SJ ;
GNANADESIKAN, R ;
KETTENRING, JR .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1981, 76 (374) :354-362
[5]  
DONG D, 1994, PROCEEDINGS OF THE 1994 AMERICAN CONTROL CONFERENCE, VOLS 1-3, P1284
[6]  
FEWIN RD, 1994, P IFAC S ADV CONTR C
[7]  
FUKUNAGA K, 1972, INTRO STATISTICAL PA
[8]  
HAMPEL F. R., 1986, Robust Statistics: The Approach Based on Influence Functions
[9]   CHEMOMETRIC METHODS FOR PROCESS MONITORING AND HIGH-PERFORMANCE CONTROLLER-DESIGN [J].
KASPAR, MH ;
RAY, WH .
AICHE JOURNAL, 1992, 38 (10) :1593-1608
[10]   MULTIVARIATE STATISTICAL MONITORING OF PROCESS OPERATING PERFORMANCE [J].
KRESTA, JV ;
MACGREGOR, JF ;
MARLIN, TE .
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 1991, 69 (01) :35-47