文献类型：期刊文献

英文题名：Parallel quality-related dynamic principal component regression method for chemical process monitoring

作者：Tao, Yang[1];Shi, Hongbo[1];Song, Bing[1];Tan, Shuai[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2019

卷号：73

起止页码：33

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20185006230525);WOS:【SCI-EXPANDED(收录号:WOS:000460809800004)】；

基金：This research is supported by the National Natural Science Foundation of China (61374140, 61673173) and Fundamental Research Funds for the Central Universities (222201717006, 222201714031).

语种：英文

外文关键词：Additive fault; Multiplicative fault; Principal; Component regression; Process monitoring

摘要：Traditional quality-related process monitoring mainly focuses on the magnitude change of the quality variables caused by additive faults. However, the abnormal fluctuations in the quality variables caused by multiplicative faults are often overlooked. In this paper, a novel parallel dynamic principal component regression (P-DPCR) algorithm is proposed to monitor the changes in the magnitude and fluctuation of the quality variables simultaneously. Firstly, in order to eliminate the interference of quality-unrelated variables, the quality-related process variables are selected on the basis of correlation analysis. Secondly, the dynamic extension and moving window are carried out for process variables and quality variables, in which the dynamic variables space (called X-space/Y-space) and the variance space (called VX-space/VY-space) are constructed. Afterwards, double quality-related statistics based on the regression model of these four spaces are given, and the comprehensive monitoring decision can be obtained. Finally, two numerical cases and the Tennessee Eastman process are used to show the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.