文献类型：期刊文献

英文题名：Local-Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data

作者：Jiang, Qingchao[1,2];Yan, Shifu[1,2];Cheng, Hui[1,2];Yan, Xuefeng[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China

年份：2021

卷号：32

期号：8

起止页码：3355

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20213210740461);WOS:【SCI-EXPANDED(收录号:WOS:000681169500011)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61973119, Grant 61603138, and Grant 21878081, in part by the Natural Science Foundation of Shanghai under Grant 16ZR1407300, and in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017.

语种：英文

外文关键词：Monitoring; Kernel; Computational modeling; Fault detection; Correlation; Principal component analysis; Big Data; Deep neural network (DNN); distributed computing; industrial big data; local-global modeling; nonlinear plant-wide processes

摘要：Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for nonlinear plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.