Extended PLS approach for enhanced condition monitoring of industrial processes

被引:68
作者
Kruger, U [1 ]
Chen, Q
Sandoz, DJ
McFarlane, RC
机构
[1] Queens Univ Belfast, Intelligent Syst & Control Grp, Belfast BT9 5AH, Antrim, North Ireland
[2] Univ Manchester, Div Mech Engn, Control Technol Ctr, Manchester, Lancs, England
[3] Invensys Performance Solut, Houston, TX 77042 USA
关键词
D O I
10.1002/aic.690470918
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
An wended partial-least squares (EPLS) algorithm is introduced to correct a deficiency of conventional partial least squares (PLS) when used as a tool to detect abnormal operating conditions in industrial processes. In the absence of feedback control, an abnormal operating condition that affects only, process response variables will not be propagated back to the process predictor (Or input) variables. Thus monitoring tools developed under the conventional PLS framework and based only, on the predictor matrix will fail to detect the abnormal condition. The EPLS algorithm described removes this deficiency by defining new scores that are based oil both predictor and response variables. The EPLS approach provides two monitoring charts to detect abnormal process behavior, as well as contribution charts to diagnose this behavior. To demonstrate the utility, of the new approach, the extended algorithm and monitoring tools are applied to a realistic simulation of a fluid catalytic cracking unit and to a real industrial process that involves a complex chemical reaction.
引用
收藏
页码:2076 / 2091
页数:16
相关论文
共 23 条
[1]   PLS regression methods [J].
Höskuldsson, Agnar .
Journal of Chemometrics, 1988, 2 (03) :211-228
[2]  
[Anonymous], 1966, MULTIVARIATE ANAL P
[3]   SOME THEOREMS ON QUADRATIC FORMS APPLIED IN THE STUDY OF ANALYSIS OF VARIANCE PROBLEMS .1. EFFECT OF INEQUALITY OF VARIANCE IN THE ONE-WAY CLASSIFICATION [J].
BOX, GEP .
ANNALS OF MATHEMATICAL STATISTICS, 1954, 25 (02) :290-302
[4]  
Box GEP., 1978, Statistics for experimenters
[5]   SIMPLS - AN ALTERNATIVE APPROACH TO PARTIAL LEAST-SQUARES REGRESSION [J].
DEJONG, S .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1993, 18 (03) :251-263
[6]   Identification of faulty sensors using principal component analysis [J].
Dunia, R ;
Qin, SJ ;
Edgar, TF ;
McAvoy, TJ .
AICHE JOURNAL, 1996, 42 (10) :2797-2812
[7]   PARTIAL LEAST-SQUARES REGRESSION - A TUTORIAL [J].
GELADI, P ;
KOWALSKI, BR .
ANALYTICA CHIMICA ACTA, 1986, 185 :1-17
[8]  
Golub G.H., 2013, MATRIX COMPUTATIONS
[9]  
Jackson JE, 1991, A user's guide to principal components
[10]   PARTIAL LEAST-SQUARES MODELING AS SUCCESSIVE SINGULAR-VALUE DECOMPOSITIONS [J].
KASPAR, MH ;
RAY, WH .
COMPUTERS & CHEMICAL ENGINEERING, 1993, 17 (10) :985-989