Multivariate statistical process control based on multiway locality preserving projections

Journal of Process Control

Volumn 18, Issue 7-8, 2008, Pages 797-807

  Hu, Kunlun ^a   Yuan, Jingqi ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Batch process monitoring; Fault detection and diagnosis; Locality preserving projections; Moving window technique; Multivariate statistical process control; Robustness

Indexed keywords

BATCH DATA PROCESSING; COMPUTATIONAL GEOMETRY; DATA REDUCTION; FAULT DETECTION; MULTIVARIABLE CONTROL SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS);

BATCH PROCESS MONITORING; FAULT DETECTION AND DIAGNOSIS; LOCALITY PRESERVING PROJECTIONS (LPP); MOVING WINDOW TECHNIQUE; MULTIVARIATE STATISTICAL PROCESS CONTROL;

STATISTICAL PROCESS CONTROL;

EID: 44649092618     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2007.11.002     Document Type: Article

References (33)

1. Nomikos P., and MacGregor J.F. Monitoring batch processes using multiway principal component analysis. AIChE Journal 40 8 (1994) 1361-1375
2. Nomikos P., and MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics 37 1 (1995) 41-59
3. Nomikos P., and MacGregor J.F. Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems 30 1 (1995) 97-108
4. Dong D., and McAvoy T.J. Nonlinear principal component analysis - based on principal curves and neural networks. Computers and Chemical Engineering 20 1 (1996) 65-78
5. Dong D., and McAvoy T.J. Batch tracking via nonlinear principal component analysis. AIChE Journal 42 8 (1996) 2199-2208
6. Jia F., Martin E.B., and Morris A.J. Non-linear principal components analysis for process fault detection. Computers and Chemical Engineering 22 suppl. 1 (1998) S851-S854
7. Lee J.M., Yoo C., and Lee I.B. Fault detection of batch processes using multiway kernel principal component analysis. Computers and Chemical Engineering 28 9 (2004) 1837-1847
8. Ku W., Storer R.H., and Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 30 1 (1995) 179-196
9. Chen J., and Liu K.C. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science 57 1 (2002) 63-75
10. Rannar S., MacGregor J.F., and Wold S. Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems 41 1 (1998) 73-81
11. Li W.H., Yue H.H., Valle-Cervantes S., and Qin S.J. Recursive PCA for adaptive process monitoring. Journal of Process Control 10 5 (2000) 471-486
12. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal 44 7 (1998) 1596-1610
13. Wang D., and Romagnoli J.A. Robust multi-scale principal components analysis with applications to process monitoring. Journal of Process Control 15 8 (2005) 869-882
14. MacGregor J.F., Christiane J., Costas K., and Koutoudi M. Process monitoring and diagnosis by multiblock PLS methods. AIChE Journal 40 5 (1994) 826-838
15. Kourti T., Nomikos P., and MacGregor J.F. Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal of Process Control 5 4 (1995) 277-284
16. Lee D.S., and Vanrolleghem P.A. Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis. Biotechnology and Bioengineering 82 4 (2003) 489-497
17. Louwerse D.J., and Smilde A.K. Multivariate statistical process control of batch processes based on three-way models. Chemical Engineering Science 55 7 (2000) 1225-1235
18. Kano M., Hasebe S., Hashimoto I., and Ohno H. Statistical process monitoring based on dissimilarity of process data. AIChE Journal 48 6 (2002) 1231-1240
19. Lee J.M., Yoo C.K., and Lee I.B. On-line batch process monitoring using different unfolding method and independent component analysis. Journal of Chemical Engineering of Japan 36 11 (2003) 1384-1396
20. Kano M., Tanaka S., Hasebe S., Hashimoto I., and Ohno H. Monitoring independent components for fault detection. AIChE Journal 49 4 (2003) 969-976
21. Lee J.M., Yoo C., and Lee I.B. Statistical process monitoring with independent component analysis. Journal of Process Control 14 5 (2004) 467-485
22. Yoo C.K., Lee J.M., Vanrolleghem P.A., and Lee I.B. On-line monitoring of batch processes using multiway independent component analysis. Chemometrics and Intelligent Laboratory Systems 71 2 (2004) 151-163
23. X.F. He, P. Niyogi, Locality preserving projections, in: Proceedings of the Conference on Advances in Neural Information Processing Systems, 8-13 December 2003, Vancouver, Canada, MIT Press, Cambridge, MA, 2004.
24. X.F. He, Locality Preserving Projections, Ph.D. Thesis, The University of Chicago, America, 2005.
25. Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15 6 (2003) 1373-1396
26. Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 (2000) 2323-2326
27. Wise B.M., and Gallagher N.B. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control 6 6 (1996) 329-348
28. Qin S.J. Statistical process monitoring: Basics and beyond. Journal of Chemometrics 17 8-9 (2003) 480-502
29. Westerhuis J.A., Gurden S.P., and Smilde A.K. Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems 51 1 (2000) 95-114
30. Van Sprang E.N.M., Ramaker H.-J., Westerhuis J.A., Gurden S.P., and Smilde A.K. Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science 57 18 (2002) 3979-3991
31. Doymaz F., Chen J., Romagnoli J.A., and Palazoglu A. A robust strategy for real-time process monitoring. Journal of Process Control 11 4 (2001) 343-359
32. Croux C., and Haesbroeck G. Principal component analysis based on robust estimators of the covariance or correlation matrix:influence functions and efficiencies. Biometrika 87 3 (2000) 603-618
33. Yuan J.Q., and Vanrolleghem P.A. Rolling learning-prediction of product formation in bioprocesses. Journal of Biotechnology 69 1 (1999) 47-62