A non-Gaussian pattern matching based dynamic process monitoring approach and its application to cryogenic air separation process

Computers and Chemical Engineering

Volumn 58, Issue , 2013, Pages 40-53

  Chen, Jingyan ^a   Yu, Jie ^a   Mori, Junichi ^a   Rashid, Mudassir M ^a   Hu, Gangshi ^b   Yu, Honglu ^b   Flores Cerrillo, Jesus ^b   Megan, Lawrence ^b  

^a MCMASTER UNIVERSITY   (Canada)

^b PRAXAIR INC   (United States)

Author keywords

Cryogenic air separation process; Dynamic process monitoring; Fault diagnosis; Independent component analysis; Mutual information; Non Gaussian pattern matching

Indexed keywords

CRYOGENIC AIR SEPARATION; DYNAMIC PROCESS MONITORING; FAULT DETECTION AND DIAGNOSIS; FAULT DETECTION AND DIAGNOSIS METHODS; INDEPENDENT COMPONENT ANALYSIS(ICA); MUTUAL INFORMATIONS; NON-GAUSSIAN; PATTERN-MATCHING TECHNIQUE;

CRYOGENICS; FAILURE ANALYSIS; GAUSSIAN NOISE (ELECTRONIC); INDEPENDENT COMPONENT ANALYSIS; PATTERN MATCHING; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS MONITORING; SEPARATION;

FAULT DETECTION;

EID: 84880017523     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2013.05.019     Document Type: Article

References (40)

1. Albazzaz H., Wang X.Z. Statistical process control charts for batch operations based on independent component analysis. Industrial & Engineering Chemistry Research 2004, 43:6731-6741.
2. AlGhazzawi A., Lennox B. Monitoring a complex refining process using multivariate statistics. Control Engineering Practice 2008, 16:294-307.
3. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal 1998, 44:1596-1610.
4. Bishop C.M. Neural networks for pattern recognition 1995, Oxford University Press, Oxford, UK.
5. Chiang L.H., Kotanchek M.E., Kordon A.K. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers & Chemical Engineering 2004, 28:1389-1401.
6. Dash S., Venkatasubramanian V. Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering 2000, 24:785-791.
7. Flores-Cerrillo J., MacGregor J.F. Multivariate monitoring of batch processes using batch-to-batch information. AIChE Journal 2004, 50:1219-1228.
8. Johannesmeyer M.C., Singhal A., Seborg D.E. Pattern matching in historical data. AIChE Journal 2002, 48:2022-2038.
9. Kano M., Hasebe S., Hashimoto I., Ohno H. Statistical process monitoring based on dissimilarity of process data. AIChE Journal 2002, 48:1231-1240.
10. Kraskov A., Stögbauer H., Grassberger P. Estimating mutual information. Physical Review E 2004, 69:066138.
11. Kraskov A., Stögbauer H., Andrzejak R.G., Grassberger P. Hierarchical clustering using mutual information. Europhysics Letters 2005, 70:278-284.
12. Ku C.-C., Lee K.Y. Diagonal recurrent neural networks for dynamic systems control. Neural Networks, IEEE Transactions on 1995, 6:144-156.
13. Kulkarni A., Jayaraman V.K., Kulkarni B.D. Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process. Computers & Chemical Engineering 2005, 29:2128-2133.
14. Lee J.-M., Yoo C.K., Lee I.-B. Statistical process monitoring with independent component analysis. Journal of Process Control 2004, 14:467-485.
15. Lennox B., Montague G.A., Hiden H.G., Kornfeld G., Goulding P.R. Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering 2001, 74:125-135.
16. Lin W., Qian Y., Li X. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Computers & Chemical Engineering 2000, 24:423-429.
17. MacGregor J.F., Kourti T. Statistical process control of multivariate processes. Control Engineering Practice 1995, 3:403-414.
18. Martin E.B., Morris A.J. Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control 1996, 6:349-358.
19. Miller J., Luyben W.L., Blouin S. Economic incentive for intermittent operation of air separation plants with variable power costs. Industrial & Engineering Chemistry Research 2008, 47:1132-1139.
20. Patton R.J., Chen J., Siew T.M. Fault diagnosis in nonlinear dynamic systems via neural networks. International Conference on Control, 1994. Vol. 2 1994, 1346-1351.
21. Qin S.J. Statistical process monitoring: Basics and beyond. Journal of Chemometrics 2003, 17:480-502.
22. Rashid M.M., Yu J. A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring. Chemometrics and Intelligent Laboratory Systems 2012, 115:44-58.
23. Roffel B., Betlem B.H.L., de Ruijter J.A.F. First principles dynamic modeling and multivariable control of a cryogenic distillation process. Computers & Chemical Engineering 2000, 24:111-123.
24. Singhal A., Seborg D.E. Pattern matching in historical batch data using PCA. Control Systems Magazine - IEEE 2002, 22:53-63.
25. Singhal A., Seborg D.E. Effect of data compression on pattern matching in historical data. Industrial & Engineering Chemistry Research 2005, 44:3203-3212.
26. Singhal A., Seborg D.E. Evaluation of a pattern matching method for the Tennessee Eastman challenge process. Journal of Process Control 2006, 16:601-613.
27. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Computers & Chemical Engineering 2003, 27:293-311.
28. Venkatasubramanian V., Rengaswamy R., Kavuri S.N., Yin K. A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Computers & Chemical Engineering 2003, 27:313-326.
29. Venkatasubramanian V., Rengaswamy R., Kavuri S.N., Yin K. A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & Chemical Engineering 2003, 27:327-346.
30. Venkatasubramanian V., Vaidyanathan R., Yamamoto Y. Process fault detection and diagnosis using neural networks-I. steady-state processes. Computers & Chemical Engineering 1990, 14:699-712.
31. Vinson D.R. Air separation control technology. Computers & Chemical Engineering 2006, 30:1436-1446.
32. Wise B.M., Gallagher N.B. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control 1996, 6:329-348.
33. Yu J. Localized Fisher discriminant analysis based complex chemical process monitoring. AIChE Journal 2011, 57:1817-1828.
34. Yu J. A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis. Journal of Process Control 2012, 22:778-788.
35. Yu J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses. Computers & Chemical Engineering 2012, 41:134-144.
36. Yu J. A support vector clustering-based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data. AIChE Journal 2013, 59:407-419.
37. Yu J., Qin S.J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal 2008, 54:1811-1829.
38. Yu J., Qin S.J. Multiway Gaussian mixture model based multiphase batch process monitoring. Industrial & Engineering Chemistry Research 2009, 48:8585-8594.
39. Yu J., Qin S.J. Variance component analysis based fault diagnosis of multi-layer overlay lithography processes. IIE Transactions 2009, 41:764-775.
40. Zhu Y., Legg S., Laird C.D. Optimal design of cryogenic air separation columns under uncertainty. Computers & Chemical Engineering 2010, 34:1377-1384.