Volume 3 Number 1
January 2006
Article Contents
Jian Shi and Xing-Gao Liu. Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA. International Journal of Automation and Computing, vol. 3, no. 1, pp. 17-22, 2006 doi:  10.1007/s11633-006-0017-9
Cite as: Jian Shi and Xing-Gao Liu. Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA. International Journal of Automation and Computing, vol. 3, no. 1, pp. 17-22, 2006 doi:  10.1007/s11633-006-0017-9

Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA

  • Received: 2005-02-28
Fund Project:

This work is supported by the National Natural Science Foundation of China (No.20106008) the National Development and Reform Commission of China (No.Fagai Gaoji-2004-2080) and the science fund for distinguished young scholars of Zhejiang University (No.111000-581645).

  • A novel soft-sensor model which incorporates PCA (principal component analysis), RBF (Radial Basis Function) networks, and MSA (Multi-scale analysis), is proposed to infer the properties of manufactured products from real process variables. PCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the melt index (MI), or quality of polypropylene produced in an actual industrial process, is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.
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    [9] R.Rallo,J.Ferre-Gine,A.Arenas,F.Giralt.Neural virtual sensor for the inferential prediction of product quality from process variables.Computers and Chemical Engineering,vol.26,no.12,pp.1735-1754,2002.
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Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA

Fund Project:

This work is supported by the National Natural Science Foundation of China (No.20106008) the National Development and Reform Commission of China (No.Fagai Gaoji-2004-2080) and the science fund for distinguished young scholars of Zhejiang University (No.111000-581645).

Abstract: A novel soft-sensor model which incorporates PCA (principal component analysis), RBF (Radial Basis Function) networks, and MSA (Multi-scale analysis), is proposed to infer the properties of manufactured products from real process variables. PCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the melt index (MI), or quality of polypropylene produced in an actual industrial process, is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.

Jian Shi and Xing-Gao Liu. Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA. International Journal of Automation and Computing, vol. 3, no. 1, pp. 17-22, 2006 doi:  10.1007/s11633-006-0017-9
Citation: Jian Shi and Xing-Gao Liu. Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA. International Journal of Automation and Computing, vol. 3, no. 1, pp. 17-22, 2006 doi:  10.1007/s11633-006-0017-9
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