A Novel Soft Sensing Method for Transient Processes Regression Utilizing Locally Weighted PLS

被引：0

作者：

He, Yuchen ^{[1
]}

Liu, Chenyang ^{[1
]}

Zhu, Binbin ^{[1
]}

Zeng, Jiusun ^{[2
]}

机构：

[1] China Jiliang Univ, Coll Mech & Elect Engn, Hangzhou 310018, Zhejiang, Peoples R China

[2] China Jiliang Univ, Coll Metrol & Measurement Engn, Hangzhou 310018, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年

关键词：

Transient process; Locally weighted partial least squares; Soft sensing; Supervised latent structure; PARTIAL LEAST-SQUARES; PRINCIPAL COMPONENT REGRESSION; SENSOR;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper develops a novel soft sensing method using locally weighted partial least squares (PLS) for transient processes regression. Industrial transient processes cannot be described using merely one model and therefore the regression model should be updated according to the online system condition. Different from previous just-in-time (JIT) methods using Euclidean distance, a supervised approach is proposed involving both process data X and quality data Y to finish sample selection tasks. The locally weighted PLS is adopted to depict the relation between X and Y. The performance of the novel soft sensing structure is validated by an industrial process.

引用

页码：1118 / 1121

页数：4

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