Treasure et al. (2004) recently proposed a new subspace-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic‐monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.
References
-
-
1)
-
Li, P., Treasure, R.J., Kruger, U.: `Dynamic principal component analysis using subspace model identification', Lect. Notes Comput. Sci., ICIC, 2005, p. 727–736, Part I, LNCS 3644.
-
2)
-
E.B. Martin ,
A.J. Morris
.
An overview of multivariate statistical process control in continuous and batch process performance monitoring.
Trans. Inst. Meas. Control
,
1 ,
51 -
60
-
3)
-
Lieftucht, D., Kruger, U., Irwin, G.: `Improved diagnosis of sensor faults using multivariate statistics', Am. Control Conf., 2004a, Boston, USA, p. 4403–4407.
-
4)
-
T. Gleason ,
R. Staelin
.
A proposal for handling missing data.
Psychometrika
,
229 -
252
-
5)
-
P. van Overschee ,
B. de Moor
.
System identification for the identification of combined deterministic-stochastic systems.
Automatica
,
1 ,
75 -
93
-
6)
-
R. Dunia ,
S.J. Qin ,
T.F. Edgar ,
T.J. McAvoy
.
Identification of faulty sensors using principal component analysis.
AIChE J.
,
10 ,
2797 -
2812
-
7)
-
R. Treasure ,
U. Kruger ,
J. Cooper
.
Dynamic multivariable statistical process control using subspace identification.
J. Process Control
,
3 ,
279 -
292
-
8)
-
R. Dunia ,
S.J. Qin
.
Subspace approach to multidimensional fault identification and reconstruction.
AIChE J.
,
8 ,
1813 -
1831
-
9)
-
W. Ku ,
R.H. Storer ,
C. Georgakis
.
Disturbance rejection and isolation by dynamic principal component analysis.
Chemomet. Intell. Lab. Syst.
,
179 -
196
-
10)
-
J.E. Jackson ,
G.S. Mudholkar
.
Control procedures for residuals associated with principal component analysis.
Technometrics
,
341 -
349
-
11)
-
B.M. Wise ,
N.B. Gallagher
.
The process chemometrics approach to process monitoring and fault detection.
J. Process Control
,
6 ,
329 -
348
-
12)
-
D. Lieftucht ,
U. Kruger ,
G.W. Irwin
.
Improved diagnosis of abnormal process behaviour using multivariate statistics, Comput. Chem. Eng..
-
13)
-
J.F. MacGregor ,
T. Kourti
.
Statistical process control of multivariate processes.
Control Eng. Pract.
,
3 ,
403 -
414
-
14)
-
Q. Chen ,
U. Kruger ,
M. Meronk ,
A.Y.T. Leung
.
Synthesis of t2 and q statistic for process monitoring.
Control Eng. Pract.
,
6 ,
745 -
755
-
15)
-
P. Miller ,
R.E. Swanson ,
C.F. Heckler
.
Contribution plots: a missing link in multivariate quality control.
Appl. Math. Comput. Sci.
,
4 ,
775 -
792
-
16)
-
T. Kourti ,
J. Lee ,
J.F. MacGregor
.
Experiences with industrial applications of projection methods for multivariate statistical process control.
Comput. Chem. Eng.
,
971 ,
745 -
750
-
17)
-
J.E. Jackson
.
Principal components and factor analysis: Part i: principal components.
J. Qual. Control
,
4 ,
201 -
213
-
18)
-
T.C. Hsia
.
(1977)
System identification.
-
19)
-
E.L. Russell ,
L.H. Chiang ,
R.D. Braatz
.
(2000)
Data-driven techniques for fault detection and diagnosis in chemical processes, Advances in industrial control.
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