Adaptive soft sensor based on time difference model and locally weighted partial least squares regression

被引：0

作者：

Yuan X. ^{[1
]}

Ge Z. ^{[1
]}

Song Z. ^{[1
]}

机构：

[1] State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang

来源：

Huagong Xuebao/CIESC Journal | 2016年 / 67卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Just-in-time learning (JITL); Locally weighted partial least squares (LWPLS); Quality prediction; Soft sensor modeling; Time difference model (TD);

D O I：

10.11949/j.issn.0438-1157.20151931

中图分类号：

学科分类号：

摘要：

Industrial process plants are often characterized with problems of variable drifts, nonlinearity and time-variant. The time difference (TD) model was proposed by researchers to handle the drifting problems. However, the global model used under TD model cannot describe the data characteristic like the time-variant and high nonlinearity well. Moreover, the prediction accuracy will greatly decrease when change of process state occurs. In this paper, the time difference model and locally weighted partial least squares (LWPLS) are synthesized to enhance the adaptability of soft sensor models. In the TD-LWPLS based soft sensor framework, TD is used to reduce the effect of process drifts. Moreover, as a just-in-time (JITL) method, LWPLS is utilized to tackle nonlinearity and change of process state. A numerical example and an industrial application example have been carried out to test the effectiveness and feasibility of the proposed method. The results demonstrate that the TD technique with the LWPLS model can achieve the best prediction accuracy in both cases compared to two other methods. © All Right Reserved.

引用

页码：724 / 728

页数：4

共 19 条

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