太原理工大学学报2017，Vol.48Issue(4)：628-633,6.DOI:10.16355/j.cnki.issn1007-9432tyut.2017.04.021
基于OBE-PLS软测量的过程自适应建模
Adaptive Soft Sensing Model Based on OBE-PLS for System Identification
摘要
Abstract
Time-varying and working-condition transition are the main problems in the industrial process.However,the static soft sensor model based on the fixed sample cannot track the current object,which leads to a poor prediction performance.In this paper,a dynamic soft sensor modeling approach based on the optimal bounding ellipsoid (OBE) and the partial least squares (PLS) algorithm was proposed.Firstly,the PLS soft sensor model based on offline data set is built.When a new query sample arrives,the statistics is established by principal component analysis (PCA) to find similar historical samples and use these similar samples to update the PLS model by OBE algorithm,so that the model achieves a good tracking effect.This method can effectively solve the problem of time-varying and working-condition transition in the process.The application results of numerical examples and the actual industrial data were given to verify the effectiveness.关键词
工况迁移/静态软测量/最优定界椭球/偏最小二乘/动态软测量Key words
working-condition transition/static soft sensing/optimal bounding ellipsoid/partial least squares/dynamic soft sensing分类
信息技术与安全科学引用本文复制引用
程瑞辉,庞宇松,乔铁柱,阎高伟..基于OBE-PLS软测量的过程自适应建模[J].太原理工大学学报,2017,48(4):628-633,6.基金项目
国家自然科学基金资助项目(61450011) （61450011）
山西省煤基重点科技攻关资助项目(MD2014-07) （MD2014-07）
山西省自然科学基金资助项目(2015011052) （2015011052）
