Multivariate process monitoring and fault diagnosis by multi-scale PCA

Computers and Chemical Engineering

Volumn 26, Issue 9, 2002, Pages 1281-1293

  Misra, Manish ^a   Yue, H Henry ^a   Qin, S Joe ^a   Ling, Cheng ^b  

^a The University of Texas at Austin   (United States)

^b UNION CARBIDE CORPORATION   (United States)

Author keywords

Fault detection; Multi scale; Principal component analysis; Sensor validation; Wavelets

Indexed keywords

BOILERS; CATALYSIS; CHEMICAL PLANTS; CORRELATION METHODS; DECOMPOSITION; PRINCIPAL COMPONENT ANALYSIS;

CHEMICAL PROCESS MONITORING; FAULT DIAGNOSIS;

CHEMICAL ENGINEERING;

CHEMICAL PLANT; FAULT DETECTION; FAULT DIAGNOSIS; MONITORING; PROCESS PLANT; RESEARCH AND DEVELOPMENT;

ARTICLE; CHEMICAL ANALYSIS; DECOMPOSITION; FLUE GAS; HEAT EXCHANGE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SENSOR; VALIDATION PROCESS; VARIANCE;

EID: 0037106519     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-1354(02)00093-5     Document Type: Article

References (37)

1. Bakshi, B. R. (1998). Multiscale PCA with applications to multivariate statistical process monitoring. The American Institute of Chemical Engineering Journal, 44(7), 1596-1610.
2. Bakshi, B. R., & Stephanopoulos, G. (1996). Compression of chemical process data by functional approximation and feature extraction. The American Institute of Chemical Engineering Journal, 42(2), 477-492.
3. Coifman R., & Saito, N. (1996). The local Karhunen-Loeve basis. Proc. IEEE International Symposium on Time-Frequency and Time-Scale Analysis, pp. 129-132, IEEE Signal Processing Society, Paris.
4. Dong, D., & McAvoy, T. J. (1996). Nonlinear principal component analysis-based on principal curves and neural networks. Computers and Chemical Engineering, 20, 65-78.
5. Dunia, R., & Qin, S. J. (1998a). Subspace approach to multidimensional fault identification and reconstruction. The American Institute of Chemical Engineering Journal, 44(8), 1813-1831.
6. Dunia, R., & Qin, S. J. (1998b). A unified geometric approach to process and sensor fault identification. Computers and Chemical Engineering, 22, 927-943.
7. Dunia, R., Qin, S. J., Edgar, T. F., & McAvoy, T. J. (1996). Identification of faulty sensors using principal component analysis. The American Institute of Chemical Engineering Journal, 42(10), 2797-2812.
8. Hastie, T. J., & Stuetzle, W. (1989). Principal curves. Journal of American Statistical Association, 84, 502-516.
9. Jackson, J. E. (1991). A user's guide to principal components. New York: Wiley-Interscience.
10. Jackson, J. E., & Mudholkar, G. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21, 341-349.
11. Kosanovich, K. A., & Piovoso, M. J. (1997). PCA of wavelet transformed process data for monitoring. Intellectual Data Analysis, 1(2), 1997.
12. Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. The American Institute of Chemical Engineering Journal, 37, 233-243.
13. Kresta, J., MacGregor, J. F., & Marlin, T. E. (1991). Multivariate statistical monitoring of process operating performance. Canadian Journal of Chemical Engineering, 69(1), 35-47.
14. Ku, W., Storer, R. H., & Georgakis, C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemical Intellectual Laboratory System, 30, 179-196.
15. Li, W., & Qin, S. J. (2001). Consistent dynamic PCA based on errors-in-variables subspace identification. Journal of Process Control, 11(6), 661-678.
16. Luo, R., Misra, M., & Himmelblau, D. M. (2002). Sensor fault detection via multiscale analysis of prediction model residuals. Industrial Engineering and Chemical Research, revised for publication.
17. Luo, R., Misra, M., & Himmelblau, D. M. (1999). Sensor fault detection via multiscale analysis and dynamic PCA. Industrial Engineering and Chemical Research, 38(4), 1489-1495.
18. Luo, R., Misra, M., Qin, S. J., Barton, R., & Himmelblau, D. M. (1998). Sensor fault detection via multiscale analysis and nonparametric statistical inference. Industrial Engineering and Chemical Research, 37(3), 1024-1032.
19. MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate processes. Controlled Engineering Practice, 3(3), 403-414.
20. MacGregor, J. F., Jaeckle, C., Kiparissides, C., & Koutoudi, M. (1994). Process monitoring and diagnosis by multiblock PLS methods. The American Institute of Chemical Engineering Journal, 40, 826-838.
21. Misra, M., Kumar, S., Qin, S. J., & Seemann, D. (2000). Recursive on-line data compression and error analysis using wavelet technology. The American Institute of Chemical Engineering Journal, 46(1), 119-132.
22. Misra, M., Kumar, S., Qin, S. J., & Seemann, D. (2001). Error based criterion for on-line wavelet data compression. Journal of Process Control, 11(6), 717-731.
23. Nimmo, I. (1995). Adequately address abnormal situation operations. Chemical Engineering Programme, 91(9), 36-45.
24. Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. The American Institute of Chemical Engineering Journal, 40(8), 1361-1375.
25. Nomikos, P., & MacGregor, J. F. (1995). Multivariate SPC charts for monitoring batch processes. Technometrics, 37(1), 41-59.
26. Nounou, M., & Bakshi, B. R. (1998). On-line multiscale filtering of random and gross errors without process models. The American Institute of Chemical Engineering Journal, 45(5), 1041-1058.
27. Piovoso, M. J., Kosanovich, K. A., & Pearson, R. K. (1992). Monitoring process performance in real time. Proceedings of ACC, 3, 2359-2363.
28. Qin, S. J., Yue, H., & Dunia, R. (1997). Self-validating inferential sensors with application to air emission monitoring. Industrial Engineering and Chemical Research, 36, 1675-1685.
29. Raich, A., & Cinar, A. (1996). Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. The American Institute of Chemical Engineering Journal, 42(4), 995-1009.
30. Saito, N. (1994). Local Feature Extraction and Its Applications Using a Library of Bases. Ph.D. thesis, Yale University.
31. Strang, G., & Nguyen, T. (1996). Wavelets and filter banks. Wellesley-Cambridge Press.
32. Tan, S., & Mavrovouniotis, M. L. (1995). Reducing data dimensionality through optimizing neural network inputs. The American Institute of Chemical Engineering Journal, 41, 1471-1479.
33. Vedam, H., & Venkatasubramanian, V. (1997). Signed digraph based multiple fault diagnosis. Computer and Chemical Engineering, 21(12), S655-S660.
34. Wickerhauser, M. V. (1994). Adapted wavelet analysis from theory to software. Wellesley, MA: A.K. Peters.
35. Wise, B. M., & Ricker, N. L. (1991). Recent advances in multivariate statistical process control: improving robustness and sensitivity. In Proceedings of the IFAC. ADCHEM Symposium (pp. 125-130).
36. Wise, B. M., Ricker, N. L., Veltkamp, D. F., & Kowalski, B. R. (1990). A theoretical basis for the use of principal component models for monitoring multivariate processes. Process Quality and Control, 1, 41-51.
37. Wold, S., Kettaneh, N., & Tjessem, K. (1996). Hierarchical multiblock PLS and PC model for easier model interpretation and as an alternative to variable selection. Journal Chemometrics, 10, 463.