文献类型：期刊文献

英文题名：Concurrent monitoring of global-local performance indicators for large-scale process

作者：Yang, Jian[1];Song, Bing[1];Tan, Shuai[1];Shi, Hongbo[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：102

起止页码：9

外文期刊名：JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS

收录：;EI(收录号：20192306998996);WOS:【SCI-EXPANDED(收录号:WOS:000479020700002)】；

基金：This research is supported by the National Natural Science Foundation of China (Nos. 61673173, 61703161); the Fundamental Research Funds for the Central Universities (No. 222201714031); the China Postdoctoral Science Foundation (No. 2017M611472).

语种：英文

外文关键词：Performance indicator monitoring; Large-scale process; Canonical Correlation Analysis; Feature selection

摘要：Multiblock methods with decision fusion are common schemes for performance indicator monitoring in large-scale process. However, single sub-block is insufficient for interpreting global performance indicator, thus the final fusion may cause inaccurate result. Besides, it is imperative to consider safety related local performance indicators (LPI) in each sub-block and important to model the correlation between each sub-block. In this paper, concurrent global-local performance indicator monitoring method is proposed. For more purposeful monitoring, this study constructs two feature subspaces, the local performance indicator related subspace (LPIRS) and the global performance indicator related subspace (GPIRS), with different significances. In LPIRS, LPI related variables in each sub-block are monitored. In GPIRS, considering the dynamic interactions between each sub-block, improved dynamic Canonical Correlation Analysis method is proposed for feature extraction. Moreover, the features are furtherly selected based on a novel selection criterion and the orthogonal decomposition on regression coefficients is employed to construct GPIRS. Finally, the effectiveness of the proposed method is validated via two cases. (C) 2019 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.