Fault detection in continuous processes using multivariate statistical methods

International Journal of Systems Science

Volumn 31, Issue 11, 2000, Pages 1459-1471

  Goulding, P R ^a   Lennox, B ^a   Sandoz, D J ^a   Smith, K J ^a   Marjanovic, O ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDBACK CONTROL; LARGE SCALE SYSTEMS; MULTIVARIABLE CONTROL SYSTEMS; NORMAL DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

FAULT DETECTION;

STATISTICAL PROCESS CONTROL;

EID: 0034334891     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/00207720050197839     Document Type: Article

References (44)

1. Basseville, M 1988, Detecting changes in signals and systems-a survey. Automatica, 24, 309-326.
2. Basseville, M., 1989, Distance measures for signal processing and pattern recognition. Signal Processing, 18, 349-369.
3. Chen, J., Bandoni, J. A., and Romagnoli, J. A., 1996, Robust PCA and normal region in multivariate statistical process monitoring. AIChE Journal, 42, 3563-3566.
4. Chen, Q., Goulding, P., Sandoz, D., and Wynne, R., 1998, Applications of density estimates to condition monitoring for the process industries. In Proc. American Control Conference, Philadelphia, PA, USA, pp. 3312-3316.
5. Chen, G., McAvoy, T. J., and Piovoso, M. J., 1998, Multivariate statistical controller for on-line quality improvement. Journal of Process Control, 8, 139-149.
6. Crowder, S. V., 1987, A simple method for studying run-length distributions of exponentially weighted moving average charts. Technometrics, 29, 401-407.
7. Dayal, B. S., and MacGregor, J. F., 1996, Identification of finite impulse response models: methods and robustness issues. Ind. Engng Chem. Res., 35, 4078-4090.
8. Dong, D., and McAvoy, T. J., 1994, Nonlinear principal component analysis based on principal curves and neural networks. In Proc. of the American Control Conference, Vol. 2, pp. 1284-1288.
9. Dunia, R., Qin, S. J., Edgar, T. F., and McAvoy, T. J., 1996, Identi® cation of faulty sensors using principal component analysis. AIChE Journal, 42, 2797-2812.
10. Efron, B., and Tibshirani, R., 1993, An Introduction to the Bootstrap (Chapman-Hall).
11. ± a survey and some new results. Eur. J. Contr., 2, 6- 28.
12. Geladi, P., and Kowalski, B. R., 1986, PLS tutorial. Anal. Chim. Acta, 185, 1-17.
13. Gibson, J. D., and Melsa, J. L., 1975, Introduction to Nonparametric Detection with Applications (Academic Press).
14. Goulding, P. R., 1995, Generic fault management project: final report. Technical Report, Control Technology Centre, University of Manchester, UK, BNFL-02/95.
15. Goulding, P. R., Lennox, B., and Sandoz, D. J., 1999, Analysis of multivariate statistical methods in application to continuous processes. (to be submitted).
16. Goulding, P. R., Riley, A., and Nixon, C., 1996, Advanced control review of the THORP reprocessing plant. Technical Report, BNFL, Sella® eld, Cumbria, UK.
17. Hand, D. J., 1981, Discrimination and Classification (Wiley).
18. Hand, D. J., 1982, Kernel Discriminant Analysis (Wiley).
19. Husmeier, D., and Taylor, J. G., 1997, Predicting conditional probability densities of stationary stochastic time series. Neural Networks, 10, 479-497.
20. Ibrahim, K. A., and Tham, M. T., 1995, Towards active statistical process control. In Proceedings of the American Control Conference, Vol. 3, pp. 2234-2238.
21. Isermann, R., 1997, Supervision, fault-detection and fault-diagnosis-an introduction. Control Engng Practice, 5, 639- 652.
22. Jackson, J.E., 1991, A Users Guide to Principal Components (Wiley).
23. Kendall, M. G., 1979-1983, The Advanced Theory of Statistics, 3rd edition (Griffin).
24. Kramer, M. A., 1992, Autoassociative neural networks. Comp. Chem. Engng, 16, 313-328.
25. Kresta, J. V., MacGregor, J. F., and Marlin, T. E., 1991, Multivariate statistical monitoring of process operating performance. Can. J. Chem. Engng, 69, 35-47.
26. Lennox, B., Goulding, P. R., and Sandoz, D. J., 1999, Application of multivariate statistical methods to continuous industrial processes. (to be submitted).
27. Lennox, B., Rutherford, P., Montague, G. A., and Haughin, C., 1998, Case study investigating the application of neural networks for process modelling and condition monitoring. Computers Chem. Engng, 22, 1573-1579.
28. Levine, W. S., 1996, The Control Handbook (Springer).
29. MacGregor, J. F., and Kourti, T., 1995, Statistical process control of multivariate processes. Contr. Engng Pr., 3, 403-414.
30. Mathai, A., and Rathie, P. N., 1975, Basic Concepts in Information Theory and Statistics (New York: Wiley).
31. Miller, P., Swanson, R. E., and Heckler, C. F., 1993, Contributions plots: the missing link in multivariate quality control. 37th Annual Fall Conference ASQC, Rochester, NY.
32. Montague, G. A., Hiden, H. G., and Kornfeld, G., 1998, Multivariate statistical monitoring procedures for fermentation supervision: an industrial case study. Proc. Conference Comp. App. in Biotechnology, Osaka, Japan.
33. Nievergelt, Y., 1994, Total least squares: state-of-the-art regression in numerical analysis. SIAM Review, 36, 258-264.
34. Nomikos, P., and MacGregor, J. F., 1995, Multivariate SPC charts for monitoring batch processes. Technometrics, 37, 44-59.
35. Piovioso, M. J., and Kosanovich, K. A., 1994, Applications of multivariate statistical methods to process monitoring and controller design. Int. J. Control, 59, 743-765.
36. Priestley, M. B., Rao, S. T., and Tong, H., 1974, Applications of principal component analysis and factor analysis in the identification of multivariable systems. IEEE Trans. AC, 19, 730-734.
37. Ripley, B. D., 1996, Pattern Recognition and Neural Networks (Cambridge; New York: Cambridge University Press).
38. Solari, M. E., 1969, The maximum likelihood solution of the problem of estimating a linear functional relationship. JR. Stat. Soc. B, 31, 372-375.
39. Sutanto, E., and Warwick, K., 1995, Cluster analysis for multivariable process control. In Proceedings of the American Control Conference, Vol. 1, pp. 749- 750.
40. Tong, H., and Crowe, C. M., 1995, Detection of gross errors in data reconciliation by principal component analysis. AIChE Journal, 41, 1712- 1722.
41. Willsky, A. S., 1976, A survey of design methods for failure detection in dynamic systems. Automatica, 12, 601-611.
42. Wilson, D. J. H., Irwin, G. W., and Lightbody, G., 1997, Nonlinear PLS using radial basis functions. Transactions of the Institute of Measurement and Control, 19, 211-220.
43. Wise, B. M., and Gallagher, N. B., 1996, The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6, 329-348.
44. Wold, S., Kettaneh-Wold, N., and Skagerberg, B., 1989, Nonlinear PLS modelling. Chemometrics and Intelligent Laboratory Systems, 7, 53-65.