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
收藏
页码：2808 / 2813
页数：6
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