文献类型：期刊文献

英文题名：Self-attention-based Multi-block regression fusion Neural Network for quality-related process monitoring

作者：Sun, Jun[1];Shi, Hongbo[1];Zhu, Jiazhen[1];Song, Bing[1];Tao, Yang[1];Tan, Shuai[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：133

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

收录：;EI(收录号：20220911719934);WOS:【SCI-EXPANDED(收录号:WOS:000793364700001)】；

基金：ACKNOWLEDGMENTS This research is supported by the National Natural Science Foun-dation of China (No. 62073140, 62073141) , Shanghai Rising-Star Pro-gram (No. 21QA1401800) , National Natural Science Foundation of Shanghai (No. 19ZR1473200) , National Natural Science Foundation of China under Grant (No. 62103149) .

语种：英文

外文关键词：Process monitoring; Quality-related feature; Self-attention Mechanism; Deep Neural Networks

摘要：Background: For plant-wide process with multiple operation units, local-global modeling is an efficient method to achieve quality-related fault detection. However, most of algorithms based on local-global modeling ignore the correlation between sub-blocks. This will result in poor performance of the extracted global quality-related features.Methods: This paper focus on the correlation between sub-blocks and proposes Self-attention-based Multi block regression fusion Neural Network (SMNN) to achieve efficient quality-related fault detection for nonlinear multi-unit process. Firstly, to focus on quality-related information, the key variables are selected. Then, to extract quality-related features in each sub-block, a pre-training approach is used, i.e. a deep neural network-based regression network between process variables and quality variables is constructed in each sub-block. Secondly, considering the correlation between the sub-blocks, self-attention mechanism is used to integrate the quality-related feature from each block. With the help of an additional regression network, the quality-related features of sub-blocks are fine-tuned and the global features are extracted. Finally, quality-related statistic is constructed to detect faults.Findings: The proposed method shows good performance in Tennessee-Eastman process, which demonstrates the effectiveness of the method. It also shows that considering the potential relationships between sub-blocks during model construction helps in the extraction of global features.(c) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.