New contributions to non-linear process monitoring through kernel partial least squares

Chemometrics and Intelligent Laboratory Systems

Volumn 135, Issue , 2014, Pages 76-89

  Godoy, José L ^a   Zumoffen, David A ^b,c   Vega, Jorge R ^a,c   Marchetti, Jacinto L ^a  

^a Instituto de Desarrollo Tecnologico para la Industria Quimica   (Argentina)

^b UNIVERSIDAD NACIONAL DE ROSARIO   (Argentina)

^c UNIVERSIDAD TECNOLÓGICA NACIONAL   (Argentina)

Author keywords

Fault detection; Fault diagnosis; KPLS modeling; Non linear processes; Prediction risk assessment

Indexed keywords

ARTICLE; CALIBRATION; KERNEL METHOD; NON LINEAR PROCESS MONITORING; PREDICTION; PRIORITY JOURNAL; PROCESS MONITORING; RISK ASSESSMENT; SENSOR; SIMULATION; STATISTICAL MODEL;

EID: 84899517383     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2014.04.001     Document Type: Article

References (34)

1. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S. A review of process fault detection and diagnosis: process history based methods. Comput. Chem. Eng. 2003, 27:327-346.
2. Zumoffen D., Basualdo M. Fault detection systems integrated to fault-tolerant control. Application to Large-scale Chemical Processes 2012, LAP Lambert Academic Publishing, Germany.
3. Kourti T. Application of latent variable methods to process control and multivariate statistical process control in industry. Int. J. Adapt. Control. Signal Proc. 2005, 19:213-246.
4. Yue H., Qin S. Reconstruction-based fault identification using a combined index. Ind. Eng. Chem. Res. 2001, 40:4403-4414.
5. Kourti T., MacGregor J. Process analysis, monitoring and diagnosis using multivariate projection methods. Chemom. Intell. Lab. Syst. 1995, 28:3-21.
6. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58:109-130.
7. Li B., Morris A., Martin E. Model selection for partial least squares regression. Chemometr. Intell. Lab. Syst. 2002, 64:79-89.
8. Zhang Y., Zhang Y. Complex process monitoring using modified partial least squares method of independent component regression. Chemom. Intell. Lab. Syst. 2009, 98:143-148.
9. Zumoffen D., Basualdo M. A systematic approach for the design of optimal monitoring systems for large scale processes. Ind. Eng. Chem. Res. 2010, 49:1749-1761.
10. Godoy J.L., Minari R.J., Vega J.R., Marchetti J.L. Multivariate statistical monitoring of an industrial SBR process. Soft-sensor for production and rubber quality. Chemom. Intell. Lab. Syst. 2011, 107:258-268.
11. Mika S., Schölkopf B., Smola A., Müller K., Scholz M., Rätsch G. Kernel PCA and de-noising in feature spaces. Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems 1999, II:536-542. MIT Press, Cambridge.
12. Kim K., Lee J., Lee I. A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction. Chemom. Intell. Lab. Syst. 2005, 79:22-30.
13. Zhao S., Zhang J., Xu Y., Xiong Z. Nonlinear projection to latent structures method and its applications. Ind. Eng. Chem. Res. 2006, 45:3843-3852.
14. Zhang X., Yan W., Shao H. Nonlinear multivariate quality estimation and prediction based on kernel partial least squares. Ind. Eng. Chem. Res. 2008, 47:1120-1131.
15. Jia M., Chu F., Wang F., Wang W. On-line batch process monitoring using batch dynamic kernel principal component analysis. Chemom. Intell. Lab. Syst. 2010, 101:110-122.
16. Alcala C., Qin S. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Ind. Eng. Chem. Res. 2010, 49:7849-7857.
17. Rosipal R., Trejo L. Kernel partial least squares regression in reproducing kernel Hilbert space. J. Mach. Learn. Res. 2001, 2:97-123.
18. Hu Y., Ma H., Shi H. Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares. Chemom. Intell. Lab. Syst. 2013, 123:15-27.
19. Zhang Y., Hu Z. On-line batch process monitoring using hierarchical kernel partial least squares. Chem. Eng. Res. Des. 2011, 89:2078-2084.
20. Williams P. Influence of water bands on prediction of composition and quality factors: the aquaphotomics of low moisture agricultural materials. J. Near Infrared Spectrosc. 2009, 17:315-328.
21. Tan C., Li M. Mutual information-induced interval selection combined with kernel partial least squares for near-infrared spectral calibration. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2008, 71:1266-1273.
22. Zhang Y., Teng Y. Process data modeling using modified kernel partial least squares. Chem. Eng. Sci. 2010, 65:6353-6361.
23. Wang Y., Li Y., Wang B. An in silico method for screening nicotine derivatives as cytochrome P450 2A6 selective inhibitors based on kernel partial least squares. Int. J. Mol. Sci. 2007, 8:166-179.
24. Embrechts M., Ekins S. Classification of metabolites with kernel-partial least squares. Drug Metab. Dispos. 2007, 35:325-327.
25. Peng K., Zhang K., Li G., Zhou D. Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process. Control. Eng. Pract. 2013, 21:360-369.
26. Qin S. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control. 2012, 36:220-234.
27. Postma G., Krooshof P., Buydens L. Opening the kernel of kernel partial least squares and support vector machines. Anal. Chim. Acta. 2011, 705:123-134.
28. Godoy J., Vega J., Marchetti J. A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space. Chemom. Intell. Lab. Syst. 2013, 128:25-36.
29. Alcala C., Qin S. Analysis and generalization of fault diagnosis methods for process monitoring. J. Process Control 2011, 21:322-330.
30. Willis A. Condition monitoring of centrifuge vibrations using kernel PLS. Comput. Chem. Eng. 2010, 34:349-353.
31. McLachlan G. Finite Mixture Models 2012, Wiley, New York.
32. Beardah C., Baxter M. MATLAB Routines for Kernel Density Estimation and the Graphical Representation of Archaeological Data, Tech. Rep 1995, Nottingham Trent University Department of Mathematics, Statistics, and Operational Research, United Kingdom.
33. Bishop C. Neural Networks for Pattern Recognition 1995, Clarendon Press, Oxford, UK.
34. Martin E., Morris A. Non-parametric confidence bounds for process performance monitoring charts. J. Process Control 1996, 6(6):349-358.