Two-Step Partial Least Squares for Monitoring Dynamic Processes

被引：1

作者：

Yuan, Zeyi ^{[1
]}

Ma, Xin ^{[1
]}

Qin, Yihao ^{[1
]}

Wang, Youqing ^{[2
]}

机构：

[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China

[2] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

基金：

中国国家自然科学基金;

关键词：

Partial least squares; dynamic process monitoring; dynamic partial least squares; two-step partial least squares (TS-PLS); COMPONENTS;

D O I：

10.1109/CCDC52312.2021.9601823

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The dynamic partial least squares (DPLS) method is widely used in dynamic industrial process monitoring. In this method, an autoregressive model is employed to describe the dynamic characteristics of a system, and traditional partial least squares (PLS) is applied to analyze correlations among data synchronously. However, DPLS only expands the dimensions of the original data; it cannot express the characteristics of the dynamic model concretely, and its monitoring performance is often unsatisfactory. The present study uses a moving average autoregressive model to describe dynamic processes and then proposes a two-step PLS (TS-PLS) algorithm to solve this problem. Testing on numerical examples and the continuous stirred-tank reactor, cases reveal that the performance of TS-PLS is much better than that of traditional DPLS.

引用

页码：2808 / 2813

页数：6