Robust supervised probabilistic principal component analysis model for soft sensing of key process variables

Chemical Engineering Science

Volumn 122, Issue , 2015, Pages 573-584

  Zhu, Jinlin ^a   Ge, Zhiqiang ^a   Song, Zhihuan ^a  

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

Author keywords

Outliers; Process data modeling; Robust modeling; Soft sensor; Supervised probabilistic model

Indexed keywords

ALGORITHMS; IMAGE SEGMENTATION; ITERATIVE METHODS; MAXIMUM PRINCIPLE; MIXTURES; STATISTICS;

OUTLIERS; PROBABILISTIC MODELING; PROCESS DATA; ROBUST MODELING; SOFT SENSORS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84910026328     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2014.10.029     Document Type: Article

References (40)

1. Agamennoni G., Nieto J.I., Nebot E.M. Approximate inference in state-space models with heavy-tailed noise. IEEE Trans. Signal Process. 2012, 60:5024-5037.
2. Ahmed F., Nazir S., Yeo Y.K. A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant. Korean J. Chem. Eng. 2009, 26:14-20.
3. Archambeau, C., Delannay, N., Verleysen, M., 2006. Robust probabilistic projections. In: Proceedings of the 23rd International Conference on Machine Learning, ACM, pp. 33-40.
4. Archambeau C., Delannay N., Verleysen M. Mixtures of robust probabilistic principal component analyzers. Neurocomputing 2008, 71:1274-1282.
5. Burnham K.P., Anderson D.R. Multimodel inference understanding AIC and BIC in model selection. Sociological Methods Res. 2004, 33:261-304.
6. Chatzis S.P., Kosmopoulos D.I., Varvarigou T.A. Robust sequential data modeling using an outlier tolerant hidden Markov model. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31:1657-1669.
7. Chen F., Wang X. Software sensor design using Bayesian automatic classification and back-propagation neural networks. Ind. Eng. Chem. Res. 1998, 37:3985-3991.
8. De La Torre F., Black M.J. A framework for robust subspace learning. Int. J. Comput. Vision 2003, 54:117-142.
9. Deng J., Huang B. Identification of nonlinear parameter varying systems with missing output data. AlChE J. 2012, 58:3454-3467.
10. Facco P., Doplicher F., Bezzo F., Barolo M. Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process. J. Process Control 2009, 19:520-529.
11. Fang Y., Jeong M.K. Robust probabilistic multivariate calibration model. Technometrics 2008, 50:305-316.
12. Fortuna L., Graziani S., Xibilia M.G. Soft sensors for product quality monitoring in debutanizer distillation columns. Control Eng. Pract. 2005, 13:499-508.
13. Fujiwara K., Kano M., Hasebe S. Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design. Control Eng. Pract. 2012, 20:371-378.
14. Ge Z., Gao F., Song Z. Mixture probabilistic PCR model for soft sensing of multimode processes. Chemom. Intell. Lab. Syst. 2011, 105:91-105.
15. Ge Z., Huang B., Song Z. Mixture semisupervised principal component regression model and soft sensor application. AlChE J. 2013.
16. Ge Z., Song Z. Mixture Bayesian regularization method of PPCA for multimode process monitoring. AlChE J. 2010, 56:2838-2849.
17. Ge Z., Song Z., Gao F. Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res. 2013, 52:3543-3562.
18. Gil J.A., Romera R. On robust partial least squares (PLS) methods. J. Chemom. 1998, 12:365-378.
19. Hodge V.J., Austin J. A survey of outlier detection methodologies. Artif. Intell. Rev. 2004, 22:85-126.
20. Hubert M., Branden K.V. Robust methods for partial least squares regression. J. Chemom. 2003, 17:537-549.
21. Hubert M., Verboven S. A robust PCR method for high-dimensional regressors. J. Chemom. 2003, 17:438-452.
22. Imtiaz S., Shah S. Treatment of missing values in process data analysis. Can. J. Chem. Eng. 2008, 86:838-858.
23. Jylänki P., Vanhatalo J., Vehtari A. Robust Gaussian process regression with a Student-t likelihood. J. Mach. Learn. Res. 2011, 12:3227-3257.
24. Kadlec P., Gabrys B., Strandt S. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 2009, 33:795-814.
25. Kaneko H., Arakawa M., Funatsu K. Development of a new soft sensor method using independent component analysis and partial least squares. AlChE J. 2009, 55:87-98.
26. Khatibisepehr S., Huang B. Dealing with irregular data in soft sensors: Bayesian method and comparative study. Ind. Eng. Chem. Res. 2008, 47:8713-8723.
27. Khatibisepehr S., Huang B., Khare S. Design of inferential sensors in the process industry: a review of Bayesian methods. J. Process Control 2013, 23:1575-1596.
28. Kim D., Lee I.-B. Process monitoring based on probabilistic PCA. Chemom. Intell. Lab. Syst. 2003, 67:109-123.
29. Lin B., Recke B., Knudsen J.K., Jørgensen S.B. A systematic approach for soft sensor development. Comput. Chem. Eng. 2007, 31:419-425.
30. Little R.J., Rubin D.B. Stat. Anal. Missing data 2002.
31. Luttinen J., Ilin A., Karhunen J. Bayesian robust PCA of incomplete data. Neural Process. Lett. 2012, 36:189-202.
32. Schafer J.L., Graham J.W. Missing data: our view of the state of the art. Psychol. Methods 2002, 7:147.
33. Svensén M., Bishop C.M. Robust Bayesian mixture modelling. Neurocomputing 2005, 64:235-252.
34. Tipping M.E., Bishop C.M. Probabilistic principal component analysis. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 1999, 61:611-622.
35. Wei X., Li C. The infinite Student's t-mixture for robust modeling. Signal Process. 2012, 92:224-234.
36. Yan W., Shao H., Wang X. Soft sensing modeling based on support vector machine and Bayesian model selection. Comput. Chem. Eng. 2004, 28:1489-1498.
37. Yu J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses. Comput. Chem. Eng. 2012, 41:134-144.
38. Yu, S., Yu, K., Tresp, V., Kriegel, H.-P., Wu, M., 2006. Supervised probabilistic principal component analysis. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 464-473.
39. Zhou, L., Song, Z., Ge, Z., Miao, A., 2013. Dynamic process monitoring based on probabilistic principle component regression. In: Control and Decision Conference (CCDC), 2013 25th Chinese, IEEE, pp. 4763-4767.
40. Zhu J., Ge Z., Song Z. Robust modeling of mixture probabilistic principal component analysis and process monitoring application. AlChE J. 2014, 60:2143-2157.