文献类型：期刊文献

英文题名：Convolutional Neural Network Based Feature Learning for Large-Scale Quality-Related Process Monitoring

作者：Zhu, Jiazhen[1];Shi, Hongbo[1];Song, Bing[1];Tao, Yang[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：2022

卷号：18

期号：7

起止页码：4555

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20214511139563);WOS:【SCI-EXPANDED(收录号:WOS:000784218500027)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62073140, Grant 62073141, and Grant 62103149, in part by the National Natural Science Foundation of Shanghai under Grant 19ZR1473200, and in part by the National Key Research and Development Program of China under Grant 2020YFC1522502 and Grant 2020YFC1522505.

语种：英文

外文关键词：Convolutional neural networks; Feature extraction; Kernel; Process monitoring; Convolution; Redundancy; Informatics; Convolutional neural network (CNN); plant wide; process monitoring; quality related

摘要：As industrial technology develops, industrial processes become increasingly large and complex, the traditional methods are difficult to extract features that can represent the condition of the whole process and the effect of fault on quality indicators. Therefore, a novel multiblock decouple convolutional neural network (multiblock DCN) algorithm is proposed. First, key process variables are selected, and process variables are grouped into multiple blocks for the following monitoring. Then, in each block, the proposed DCN constructs a regression model between key process variables and quality indicators, in which the regression model utilizes an improved convolutional neural network as a feature extractor and a decoupling layer as a feature regularizer. Afterward, the monitoring results of each block are integrated into a global monitoring index based on Bayesian theory. After fault detection, variable oblivion contribution plot is presented to locate faulty variables. Finally, two industrial cases are used to demonstrate the effectiveness of multiblock DCN.