Statistical monitoring of dynamic processes based on dynamic independent component analysis

Chemical Engineering Science

Volumn 59, Issue 14, 2004, Pages 2995-3006

  Lee, Jong Min ^a   Yoo, ChangKyoo ^b   Lee, In Beum ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Fault detection; Independent component analysis (ICA); Principal component analysis (PCA); Process control; Process monitoring; Systems engineering

Indexed keywords

COMPUTER SIMULATION; CORRELATION METHODS; DATA ACQUISITION; MATRIX ALGEBRA; MEASUREMENT THEORY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL METHODS;

DYNAMIC PROCESSES; GAUSSIAN DISTRIBUTION; STATISTICAL MONITORING;

CHEMICAL ENGINEERING;

MONITORING; PROCESS CONTROL;

EID: 3042632377     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2004.04.031     Document Type: Article

References (33)

1. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring American Institute of Chemical Engineering Journal 44 7 1998 1596-1610
2. Chen G. McAvoy T.J. Predictive on-line monitoring of continuous processes Journal of Process Control 8 1998 409-420
3. Chen J. Liao C.-M. Dynamic process fault monitoring based on neural network and PCA Journal of Process Control 12 2002 277-289
4. Chiang L.H. Braatz R.D. Process monitoring using causal map and multivariate statistics: Fault detection and identification Chemometrics and Intelligent Laboratory Systems 65 2003 159-178
5. Chiang L.H. Russell E.L. Braatz R.D. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis Chemometrics and Intelligent Laboratory Systems 50 2000 243-252
6. Chiang L.H. Russell E.L. Braatz R.D. Fault Detection and Diagnosis in Industrial Systems 2001 Springer London
7. Dong D. McAvoy T.J. Nonlinear principal component analysis-based on principal curves and neural networks Computers and Chemical Engineering 20 1 1996 65-78
8. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis IEEE Transactions on Neural Networks 10 1999 626-634
9. Hyvärinen A. Oja E. Independent component analysis: Algorithms and applications Neural Networks 13 4-5 2000 411-430
10. Hyvärinen A. Karhunen J. Oja E. Independent Component Analysis 2001 Wiley New York
11. Kano M. Hasebe S. Hashimoto I. Ohno H. Statistical process monitoring based on dissimilarity of process data American Institute of Chemical Engineering Journal 48 6 2002 1231-1240
12. Kano M. Tanaka S. Hasebe S. Hashimoto I. Ohno H. Monitoring independent components for fault detection American Institute of Chemical Engineering Journal 49 4 2003 969-976
13. Kano M. Hasebe S. Hashimoto I. Ohno H. Evolution of multivariate statistical process control: Application of independent component analysis and external analysis Computers and Chemical Engineering 28 6-7 2004 1157-1166
14. Kresta J. MacGregor J.F. Marlin T.E. Multivariate statistical monitoring of process operating performance Canadian Journal of Chemical Engineering 69 1991 35-47
15. Ku W. Storer R.H. Georgakis C. Disturbance detection and isolation by dynamic principal component analysis Chemometrics and Intelligent Laboratory Systems 30 1995 179-196
16. Lee J.-M. Yoo C.K. Lee I.-B. Statistical process monitoring with independent component analysis Journal of Process Control 14 5 2003 467-485
17. Lee J.-M. Yoo C.K. Lee I.-B. Statistical process monitoring with multivariate exponentially weighted moving average and independent component analysis Journal of Chemical Engineering of Japan 36 5 2003 563-577
18. Lee T. Independent Component Analysis: Theory and Applications 1998 Kluwer Academic Publishers Boston
19. Li W. Yue H. Cervantes S.V. Qin J. Recursive PCA for adaptive process monitoring Journal of Process Control 10 2000 471-486
20. Luo R. Misra M. Himmelblau D.M. Sensor fault detection via multiscale analysis and dynamic PCA Industrial and Engineering Chemistry Research 38 1999 1489-1495
21. Lyman P.R. Georgakis C. Plant-wide control of the Tennessee eastman problem Computers and Chemical Engineering 19 1995 321-331
22. Martin E.B. Morris A.J. Non-parametric confidence bounds for process performance monitoring charts Journal of Process Control 6 6 1996 349-358
23. Negiz A. Cinar A. Statistical monitoring of multivariable dynamic processes with state-space models American Institute of Chemical Engineering Journal 43 8 1997 2002-2020
24. Nomikos P. MacGregor J.F. Monitoring batch processes using multiway principal component analysis American Institute of Chemical Engineering Journal 40 8 1994 1361-1375
25. Raich A.C. Çinar A. Multivariate statistical methods for monitoring continuous processes: Assessment of discriminatory power disturbance models and diagnosis of multiple disturbances Chemometrics and Intelligent Laboratory Systems 30 1995 37-48
26. Raich A.C. Çinar A. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes American Institute of Chemical Engineering Journal 42 1996 995-1009
27. Russell E.L. Chiang L.H. Braatz R.D. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis Chemometrics and Intelligent Laboratory Systems 51 2000 81-93
28. Silverman B.W. Density Estimation for Statistics and Data Analysis 1986 Chapman - Hall UK
29. Simoglou A. Martin E.B. Morris A.J. Statistical performance monitoring of dynamic multivariate processes using state space modeling Computers and Chemical Engineering 26 6 2002 909-920
30. Tsung F. Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA International Journal of Production Research 38 3 2000 625-637
31. Wise B.M. Gallagher N.B. The process chemometrics approach to process monitoring and fault detection Journal of Process Control 6 6 1996 329-348
32. Wold S. Cross-validatory estimation of components in factor and principal components models Technometrics 20 1978 397-405
33. Yoo C.K. Choi S.W. Lee I.-B. Dynamic monitoring method for multiscale fault detection and diagnosis in MSPC Industrial and Engineering Chemistry Research 41 2002 4303-4317