Data-based linear Gaussian state-space model for dynamic process monitoring

AIChE Journal

Volumn 58, Issue 12, 2012, Pages 3763-3776

  Wen, Qiaojun ^a   Ge, Zhiqiang ^a   Song, Zhihuan ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Dynamic; Fault detection, fault identification; Linear Gaussian state space model; Probabilistic

Indexed keywords

DYNAMIC DATA; DYNAMIC PROCESS; DYNAMIC PROCESS MONITORING; EFFECT ANALYSIS; EXPECTATION-MAXIMIZATION ALGORITHMS; FAULT DETECTION AND IDENTIFICATION; FAULT IDENTIFICATIONS; GAUSSIANS; IDENTIFICATION METHOD; MODEL PARAMETERS; NOISY ENVIRONMENT; PROBABILISTIC; STATE-SPACE MODELS;

ALGORITHMS; DYNAMICS; GAUSSIAN DISTRIBUTION; PROCESS MONITORING;

ITERATIVE METHODS;

EID: 84868704032     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.13776     Document Type: Article

References (36)

1. Kano M, Nagao K, Hasebe H, Hashimoto I, Ohno H, Strauss R, Bakshi BR. Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Comput Chem Eng. 2002; 26: 161-174.
2. Qin SJ. Statistical process monitoring: basics and beyond. J Chemom 2003; 17: 480-502.
3. Wang XZ, Medasani S, Marhoon F, Albazzaz H. Multidimensional visualization of principal component scores for process historical data analysis. Ind Eng Chem Res. 2004; 43: 7036-7048.
4. MacGregor JF, Yu HL, Munoz SG, Flores-Cerrillo J. Data-based latent variable methods for process analysis, monitoring and control. Comput Chem Eng. 2005; 29: 1217-1223.
5. Singhai A, Seborg DE. Evaluation of a pattern matching method for the Tennessee Eastman challenge process. J Process Control. 2006; 16: 601-613.
6. Doan XT, Srinivasan R. Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Comput Chem Eng. 2008; 32: 230-243.
7. He QP, Wang J. Statistics pattern analysis: a new process monitoring framework and its application to semiconductor batch processes. AIChE J. 2011; 57: 107-121.
8. Yu J. Nonlinear bioprocess monitoring using multiway kernel localized fisher discriminant analysis. Ind Eng Chem Res. 2011; 50: 3390-3402.
9. Bishop CM. Pattern Recognition and Machine Learning. Springer, Singapore, 2006.
10. Kim D, Lee IB. Process monitoring based on probabilistic PCA. Chem Intell Lab Syst. 2003; 67: 109-123.
11. Narasimhan S, Shah SL. Model identification and error covariance matrix estimation from noisy data using PCA. Control Eng Pract. 2008; 16: 146-155.
12. Feital T, Kruger U, Xie L, Schubert U, Lima EL, Pinto JC. A unified statistical framework for monitoring multivariate systems with unknown source and error signals. Chem Intell Lab Syst. 2010; 104: 223-232.
13. Kim DS, Yoo CK, Kim YI, Jung JK, Lee IB. Calibration, prediction and process monitoring model based on factor analysis for incomplete process data. J Chem Eng Jpn. 2005; 38: 1025-1034.
14. Ge ZQ, Xie L, Song ZH. A novel statistical-based monitoring approach for complex multivariate processes. Ind Eng Chem Res. 2009; 48: 4892-4898.
15. Ku W, Storer RH, Georgakis C. Disturbance rejection and isolation by dynamic principal component analysis. Chem Intell Lab Syst. 1995; 30: 179-196.
16. Chen J, Liu K. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chem Eng Sci. 2002; 14: 63-75.
17. Choi SW, Lee IB. Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chem Eng Sci. 2004; 59: 5897-5908.
18. Jia MX, Chu F, Wang FL, Wang W. On-line batch process monitoring using batch dynamic kernel principal component analysis. Chem Intell Lab Syst. 2010; 101: 110-122.
19. Zhang YW, Li ZW, Zhou H. Statistical analysis and adaptive technique for dynamic process monitoring. Chem Eng Res Des. 2010; 88: 1381-1392.
20. Kruger U, Zhou YQ, Irwin GW. Improved principal component monitoring of large-scale processes. J Process Control. 2004; 14: 879-888.
21. Xie L, Kruger U, Lieftucht D, Littler T, Chen Q, Wang SQ. Statistical monitoring of dynamic multivariate processes part1 modeling autocorrelation and cross-correlation. Ind Eng Chem Res. 2006; 45: 1659-1676.
22. Yao Y, Gao FR. Subspace identification for two-dimensional dynamic batch process statistical monitoring. Chem Eng Sci. 2008; 63: 3411-3418.
23. Odiowei PP, Cao Y. State-space independent component analysis for nonlinear dynamic process monitoring. Chem Intell Lab Syst. 2010; 103: 59-65.
24. Alabi SI, Morris AJ, Martin EB. On-line dynamic process monitoring using wavelet-based generic dissimilarity measure. Chem Eng Res Des. 2005; 83: 698-705.
25. Choi SW, Morris J, Lee IB. Dynamic model-based batch process monitoring. Chem Eng Sci. 2008; 63: 622-636.
26. Ge ZQ, Kruger U, Lamont L, Xie L, Song ZH. Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches. Mech Syst Signal Process. 2010; 24: 2972-2984.
27. Alcala CF, Qin SJ. Reconstruction-based contribution fro process monitoring. Automatica. 2009; 45: 1593-1600.
28. Roweis S, Ghahramani Z. A unifying review of linear Gaussian models. Neural Comput. 1999; 11: 305-345.
29. Barber D, Chiappa S. Unified inference for variational Bayesian linear Gaussian state-space model. In: Schölkopf B, Platt J, Hoffman T, editors. Advances in Neural Information Processing Systems 19. Cambridge, MA: MIT Press, 2007: 81-88.
30. Anderson BDO, Moore JB. Optimal Filtering. Englewood Cliffs, NJ: Prentice-Hall, Inc., 1979.
31. Miller P, Swanson RE, Heckler CF. Contribution plots: the missing link in multivariate quality control. In: Fall Conf. of the ASQC and ASA. Milwaukee, WI, 1993.
32. Dunia R, Qin SJ, Edgar T, McAvoy T. Identification of faulty sensors using PCA. AIChE J. 1996; 42: 2797-2812.
33. Yue H, Qin SJ. Reconstruction based fault identification using a combined index. Ind Eng Chem Res. 2001; 40: 4403-4414.
34. Chen T, Sun Y. Probabilistic contribution analysis for statistical process monitoring: a missing variable approach. Control Eng Pract. 2009; 17: 469-477.
35. Liu J. Data-driven fault detection and isolation for multimode processes. Asia-Pacific J Chem Eng. 2011; 6: 470-483.
36. Downs JJ, Vogel EF. A plant-wide industrial process control problem. Comput Chem Eng. 1993; 17: 245-255.