基于非高斯信息的JITL软测量模型

上海交通大学学报（自然版） ›› 2015, Vol. 49 ›› Issue (06): 897-901.

• 自动化技术、计算机技术 • 上一篇下一篇

基于非高斯信息的JITL软测量模型

李元,张新民

（沈阳化工大学信息工程学院, 沈阳 110142）

收稿日期:2015-01-10
基金资助:
国家自然科学基金重点项目（61034006），国家自然科学基金项目（61174119,60774070）

Non-Gaussian Information Based JITL Soft Sensor Model

LI Yuan,ZHANG Xinmin

（College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China）

Received:2015-01-10

摘要/Abstract

摘要：

摘要：为了有效监控具有非高斯数据特性的工业过程，提出了一种新的基于非高斯信息的JITL（JustInTime Learning）软测量模型.首先通过非高斯非相似度测量选择JITL局部建模样本；然后建立局部ICA-PLS回归模型实现工业过程质量变量监控.该方法从局部建模样本选择到局部回归模型建立能够有效处理工业过程数据的非高斯特性，并且保留了JITL建模的优点，能够有效地处理工业过程时变特性以及非线性.通过硫回收处理过程的应用，验证了方法的有效性.

关键词: 非高斯非相似度测量, JITL, 质量预测, 硫回收处理过程

Abstract:

Abstract： In order to monitor the non-Gaussian industrial process, a novel non-Gaussian information based JITL soft sensor model was proposed in this paper. First, the non-Gaussian dissimilarity measure selects the most relevant local modeling samples of JITL model. Then, an ICA-PLS regression method was established on the most relevant local samples for quality variable prediction. From the local relevant sample selection to the final regression model construction, the proposed method can efficiently extract the higher-order statistical information and is well suited for the non-Gaussian process quality prediction. Meanwhile, the proposed method can well cope with the changes in process characteristics as well as nonlinearity. The validity of the proposed method was verified on the sulfur recovery unit.

Key words: non-Gaussian dissimilarity measure, just-in-time learning(JITL), quality prediction, sulfur recovery unit

中图分类号:

TP 274

李元,张新民. 基于非高斯信息的JITL软测量模型[J]. 上海交通大学学报（自然版）, 2015, 49(06): 897-901.

LI Yuan,ZHANG Xinmin. Non-Gaussian Information Based JITL Soft Sensor Model[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 897-901.

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