Nonlinear multiscale modelling for fault detection and identification

Chemical Engineering Science

Volumn 63, Issue 8, 2008, Pages 2252-2266

  Choi, Sang Wook ^a   Morris, Julian ^b   Lee, In Beum ^c  

^a SAMSUNG Electronics   (South Korea)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

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

Author keywords

Fault detection and diagnosis; Kernel principal component analysis; Multiresolution analysis; Multiscale kernel principal component analysis; Multivariate statistical process control

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; MULTIRESOLUTION ANALYSIS; STATISTICAL METHODS;

KERNEL PRINCIPAL COMPONENT ANALYSIS; MULTISCALE KERNEL PRINCIPAL COMPONENT ANALYSIS; MULTIVARIATE STATISTICAL PROCESS CONTROL;

FAULT DETECTION;

COMPUTER SIMULATION; FAULT DETECTION; MATHEMATICAL MODELS; MULTIRESOLUTION ANALYSIS; STATISTICAL METHODS;

EID: 40949103011     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2008.01.022     Document Type: Article

References (35)

1. Aradhye H.B., Bakshi B.R., Strauss R.A., and Davis J.F. Multiscale SPC using wavelets: theoretical analysis and properties. A.I.Ch.E. Journal 49 4 (2003) 939-958
2. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. A.I.Ch.E. Journal 44 7 (1998) 1596-1610
3. Box G.E.P. Some theorems on quadratic forms applied on the study of analysis of variance problems: effect of inequality of variance in one-way classification. The Annals of Mathematical Statistics 25 (1954) 290-302
4. Cho J.-H., Lee J.-M., Choi S.W., Lee D., and Lee I. Fault identification for process monitoring using kernel principal component analysis. Chemical Engineering Science 60 (2005) 279-288
5. Choi S., and Lee I. Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chemical Engineering Science 59 (2004) 5897-5908
6. Choi S.W., Lee C., Lee J.-M., and Lee I. Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems 75 (2005) 55-67
7. Choi S.W., Martin E.B., Morris A.J., and Lee I. Fault detection based on maximum-likelihood principal component analysis (PCA) mixture. Industrial and Engineering Chemistry Research 44 (2005) 2316-2327
8. Choi, S.W., Martin, E.B., Morris, A.J., Lee, I.-B., 2006. Non-linear multiscale fault detection and identification. In: 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Beijing, pp. 145-150.
9. Dong D., and McAvoy T.J. Non-linear principal component analysis-based on principal curves and neural networks. Computers and Chemical Engineering 20 (1996) 65-78
10. Hubbard R., and Bayarri M.J. Confusion over measures of evidence (p's) versus errors (alpha's) in classical statistical testing. American Statistician 57 (2003) 171-178
11. Jia F., Martin E.B., and Morris A.J. Non-linear principal component analysis for process fault detection. Computers and Chemical Engineering 22 (1998) S851-S854
12. Kano M., Hasebe S., Hashimoto I., and Ohno H. Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Computers and Chemical Engineering 28 (2004) 1157-1166
13. Kosanovich K.A., and Piovoso M.J. PCA of wavelet transformed process data for monitoring. Intelligent Data Analysis 1 (1997) 85-99
14. Ku W.F., Storer R.H., and Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 30 (1995) 179-196
15. Mallet S.G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 7 (1989) 674-693
16. Marlin T.E. Process Control: Designing Processes and Control Systems for Dynamic Performances (1995), McGraw-Hill, New York
17. McPherson, L., Martin, E.B., Morris, A.J., 2002. Super model based techniques for batch process performance monitoring. In: Proceedings of the 12th European Symposium on Computer Aided Process Engineering (ESCAPE 12), pp. 523-528.
18. Miller P., Swanson R.E., and Heckler C.F. Contribution plots: a missing link in multivariate quality control. International Journal of Applied Mathematics and Computer Science 8 (1998) 775-792
19. Misra M., Yue H.H., Qin S.J., and Ling C. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers and Chemical Engineering 26 9 (2002) 1281-1293
20. Müller K.-R., Mika S., Rätsch G., Tsuda K., and Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12 (2001) 181-201
21. Negiz A., and Cinar A. Statistical monitoring of multivariable dynamic processes with state space models. A.I.Ch.E. Journal 43 8 (1997) 209-221
22. Nomikos P., and MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics 37 (1995) 41-59
23. Rakotomamonjy A. Variable selection using SVM-based criteria. Journal of Machine Learning Research 3 (2003) 1357-1370
24. Russell E.L., Chiang L.H., and 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
25. Schölkopf B., Smola A.J., and Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 (1998) 1299-1319
26. Shin H.J., Eom D.-H., and Kim S.-S. One-class support vector machines-an application in machine fault detection and classification. Computers & Industrial Engineering 48 (2005) 395-408
27. Simoglou A., Martin E.B., and Morris A.J. Statistical performance monitoring of dynamic multivariate processes using state space modelling. Computers and Chemical Engineering 26 (2002) 909-920
28. Strang G., and Nguyen T. Wavelets and Filter Banks (1996), Wellesley-Cambridge Press, Wellesley, MA
29. Tamura M., and Tsujita S. A study on the number of principal components and sensitivity of fault detection using PCA. Computers and Chemical Engineering 31 (2006) 1035-1046
30. Teppola P., and Minkkinen P. Wavelet-PLS regression models for both exploratory data analysis and process monitoring. Journal of Chemometrics 14 (2000) 383-399
31. Theodoridis S., and Koutroumbas K. Pattern Recognition (1998), Academic Press, CA
32. Tracy N.D., Young J.C., and Mason R.L. Multivariate control charts for individual observations. Journal of Quality Technology 24 2 (1992) 88-95
33. Wang C. Sense and Nonsense of Statistical Inference: Controversy, Misuse, and Subtlety (1993), Marcel Dekker Inc., New York
34. Yoon S., and MacGregor J.F. Fault diagnosis with multivariate statistical models part I: using steady state fault signatures. Journal of Process Control 11 (2001) 387-400
35. Yoon S., and MacGregor J.F. Principal-component analysis of multiscale data for process monitoring and fault diagnosis. A.I.Ch.E. Journal 50 11 (2004) 2891-2903