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

作　　者：Jian Shi Xing-Gao Liu 

机构地区：[1]National Laboratory of Industrial Control Technology, Institute of Systems Enginccring, Zhejiang University, Hangzhou 310027, PRC

出　　处：《International Journal of Automation and computing》2006年第1期17-22,共6页国际自动化与计算杂志（英文版）

基　　金：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).

摘　　要：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.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.

关 键 词：PCA RBF MSA POLYPROPYLENE MI. 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]