Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring

Journal of Process Control

Volumn 20, Issue 3, 2010, Pages 344-359

  Yu, Jianbo ^a  

^a SHANGHAI UNIVERSITY   (China)

Author keywords

Bayesian inference; Hidden Markov models; Independent component analysis; Nonlinear and multimodal process monitoring; Principal component analysis

Indexed keywords

BAYESIAN INFERENCE; CONVENTIONAL APPROACH; DATA DISTRIBUTION; FAULT PATTERNS; GAUSSIAN COMPONENTS; GAUSSIAN DISTRIBUTED; GLOBAL INFORMATIONS; HIDDEN STATE; HUMAN INTERVENTION; IN-PROCESS; INDUSTRIAL PROCESSS; LOCAL INFORMATION; LOG LIKELIHOOD; MAHALANOBIS DISTANCES; MONITORING MODELS; MULTI-MODAL; MULTIVARIATE STATISTICAL PROCESS MONITORING; NONLINEAR AND MULTIMODAL PROCESS MONITORING; NORMAL OPERATIONS; ON-LINE PROCESS MONITORING; OPERATING DATA; POSTERIOR PROBABILITY; PROCESS FAILURE; PROCESS STATE; REAL-WORLD PROCESS; UNIMODAL;

BAYESIAN NETWORKS; INDEPENDENT COMPONENT ANALYSIS; INFERENCE ENGINES; MULTIVARIANT ANALYSIS; NONLINEAR ANALYSIS; NORMAL DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; PROBABILITY DENSITY FUNCTION; PROCESS MONITORING; STATISTICAL PROCESS CONTROL;

HIDDEN MARKOV MODELS;

EID: 75149115338     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2009.12.002     Document Type: Article

References (57)

1. Qin S.J. Statistical process monitoring: basics and beyond. J. Chemom. 17 (2003) 480-502
2. Yu J., and Qin S.J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AICHE J. 54 7 (2008) 1811-1829
3. Paulonis M.A., and Cox J.W. A practical approach for large-scale controller performance assessment, diagnosis, and improvement. J. Process Control 13 (2003) 155-168
4. Qin S.J., and Yu J. Recent developments in multivariable controller performance monitoring. J. Process Control 17 (2007) 221-227
5. Montgomery D.C. Introduction to Statistical Quality Control. third ed. (1997), Wiley, New York
6. Jackson J.E. A User's Guide to Principal Components (1991), Wiley, New York
7. Nomikos P., and MacGregor J. Multivariate SPC charts for monitoring batch processes. Technometrics 37 1 (1995) 41-59
8. Cherry G.A., and Qin S.J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis. IEEE Trans. Semicond. Manuf. 19 2 (2006) 159-172
9. Chiang L.H., Russell E.L., and Braatz R.D. Fault Detection and Diagnosis in Industrial Systems, Advanced Textbooks in Control and Signal Processing (2001), Springer Verlag, London, Great Britain
10. Li W., Yue H., Valle S., and Qin J. Recursive PCA for adaptive process monitoring. J. Process Control 10 (2000) 471-486
11. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. AIChE J. 44 (1998) 1596-1610
12. Wise B.M., and Gallagher N.B. The process chemometrics approach to process monitoring and fault detection. J. Process Control 6 6 (1996) 329-348
13. MacGregor J.F., Jaeckle C., Kiparissides C., and Koutoudi M. Process monitoring and diagnosis by multiblock PLS methods. AIChE J. 40 (1994) 826-838
14. Lee J.M., Yoo C.K., and Lee I.B. Statistical process monitoring with independent component analysis. J. Process Control 14 (2004) 467-485
15. Lee J.M., Qin S.J., and Lee I.B. Fault detection and diagnosis based on modified independent component analysis. AIChE J. 52 (2006) 3501-3514
16. Simoglou A., Martin E.B., and Morris A.J. Statistical performance monitoring of dynamic multivariate processes using state space modeling. Comput. Chem. Eng. 26 (2002) 909-920
17. Kano M., Hasebe S., and Hashimoto I. Statistical process monitoring based on dissimilarity of process data. AICHE J. 48 6 (2002) 1231-1240
18. Hu K., and Yuan J. Multivariate statistical process control based on multiway locality preserving projections. J. Process Control 18 (2008) 797-807
19. Kramer M.A. Autoassociative neural networks. Comput. Chem. Eng. 16 (1992) 313-328
20. Rengaswamy R., and Venkatasurbramanian V. A fast training neural network and its updation for incipient fault detection and diagnosis. Comput. Chem. Eng. 24 (2000) 431-437
21. Aradhye H.B., Bakshi B.R., Davis J.F., and Ahalt S.C. Clustering in wavelet domain: a multiresolution ART network for anomaly detection. AICHE J. 50 10 (2004) 2455-2466
22. Chiang L., Kotanchek M., and Kordon A. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Comput. Chem. Eng. 28 (2004) 1389-1401
23. Mehranbod N., Soroush M., Piovoso M., and Ogunnaike B.A. Probabilistic model for sensor fault detection and identification. AIChE J. 49 (2003) 1787-1802
24. Dong D., and McAvoy T.J. Nonlinear principal component analysis based on principal curves and neural networks. Comput. Chem. Eng. 20 1 (1996) 65-78
25. Hwang D.H., and Han C. Real-time monitoring for a process with multiple operating modes. Control Eng. Pract. 7 (1999) 891-902
26. He Q.P., and Wang J. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf. 20 4 (2007) 345-354
27. Krammer M.A. Nonlinear principal component analysis using autoassociative neural networks. AICHE J. 37 2 (1991) 233-243
28. Lee J.M., Yoo C.K., Choi S.W., Vanrolleghem P.A., and Lee I.B. Nonlinear process monitoring using kernel principal component analysis. Chem. Eng. Sci. 59 (2004) 223-234
29. Choi S.W., and Lee I.B. Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chem. Eng. Sci. 59 (2004) 5897-5908
30. Lee J.M., Qin S.J., and Lee I.B. Fault detection of nonlinear processes using kernel independent component analysis. Can. J. Chem. Eng. 85 (2007) 526-536
31. Martin E.B., and Morris A.J. Non-parametric confidence bounds for process performance monitoring charts. J. Process Control 6 (1996) 349-358
32. Hao J.D., Rong G., and Lee J.M. Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis. Chem. Intell. Lab. Syst. 96 (2009) 75-83
33. Lane S., Martin E.B., Kooijmans R., and Morris A.J. Performance monitoring of a multiproduct semi-batch process. J. Process Control 11 (2001) 1-11
34. Wise B.M., Gallagher N.B., Butler S.W., White Jr. D.D., and Barna G.G. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. J. Chemom. 13 3-4 (1999) 379-396
35. Zhao S.J., Zhang J., and Xu Y.M. Monitoring of processes with multiple operating modes through multiple principal component analysis models. Ind. Eng. Chem. Res. 43 (2004) 7025-7035
36. Chen J., and Liu J. Mixture principal component analysis models for process monitoring. Ind. Eng. Chem. Res. 38 (1999) 1478-1488
37. Choi S.W., Park J.H., and Lee I.B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Comput. Chem. Eng. 28 (2004) 1377-1387
38. Chen T., Morris J., and Martin E.B. Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring. Appl. Statist. 55 5 (2006) 699-715
39. Bakhtazad A., Palazoglu A., and Romagnoli J.A. Detection and classification of abnormal process situation using multidimensional wavelet domain hidden Markov trees. Comput. Chem. Eng. 24 (2000) 769-775
40. Sum W., Palazoglu A., and Romagnoli J.A. Detecting abnormal process trends by wavelet-domain hidden Markov models. AICHE J. 49 1 (2003) 140-150
41. Chen J., and Chang W.J. Applying wavelet-based hidden Markov tree to enhancing performance of process monitoring. Chem. Eng. Sci. 60 (2005) 5129-5143
42. Chen J., and Chen H.H. On-line batch process monitoring using MHMT-based MPCA. Chem. Eng. Sci. 61 (2006) 3223-3239
43. Wold S., Esbensen K., and Geladi P. Principal component analysis. Chemom. Intell. Lab. Syst. 2 (1987) 37-52
44. Johnson R.A., and Wichen D.W. Applied Multivariate Statistical Analysis (1992), Englewood Cliffs, Prentice-Hall, NJ
45. Jackson J.E., and Mudholkar G.S. Control procedures for residuals associated with principal component analysis. Technometrics 21 (1972) 341-349
46. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77 2 (1989) 257-286
47. Poritz A.B. Hidden Markov models: a guided tour. ICASSP 1 (1988) 7-13
48. Viterbi A.J. Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Trans. Informat. Theory 13 (1967) 260-269
49. Schwarz G. Estimating the dimension of a model. Ann. Stat. 6 2 (1978) 461-464
50. Akaike H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19 6 (1974) 716-723
51. Yu J.B., and Xi L.F. Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes. Int. J. Prod. Res. 46 21 (2008) 5907-5933
52. Yu J.B., and Wang S.J. Using minimum quantization error chart for the monitoring of process states in multivariate manufacturing processes. Comput. Ind. Eng. 57 4 (2009) 1300-1312
53. Eigenvector Res. Inc., Metal Etch Data for Fault Detection Evaluation, 1999. .
54. Down J.J., and Vogel E.F. Plant-wide industrial process control problem. Comput. Chem. Eng. 17 (1993) 245-255
55. Tennessee Eastman Process Data, 2002. .
56. Hyvarinen A., and Oja E. Independent component analysis: algorithms and applications. Neural Networks 13 4-5 (2000) 411-430
57. Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis: algorithms and applications. IEEE Trans. Neural Networks 10 (1999) 626-634