文献类型：期刊文献

英文题名：Distributed-ensemble stacked autoencoder model for nonlinear process monitoring

作者：Li, Zhichao[1];Tian, Li[1];Jiang, Qingchao[1];Yan, Xuefeng[1]

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

年份：2021

卷号：542

起止页码：302

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20203108994558);WOS:【SCI-EXPANDED(收录号:WOS:000573606500002)】；

基金：The authors are grateful for the support of National Natural Science Foundation of China (21878081) and Fundamental Research Funds for the Central Universities under Grant of China (222201917006).

语种：英文

外文关键词：Stacked autoencoder; Process monitoring; Distributed ensemble; Joint feature

摘要：Determining whether a fault occurs locally or globally is highly important for large-scale industrial processes involving multiple operating units. Moreover, the complex nonlinearity among process variables is a prominent feature of modern industries. This paper proposes a distributed-ensemble stacked autoencoder (DE-SAE) model based on deep learning technology for monitoring non-linear, large-scale, multi-unit processes. First, the deep features of the variables involved in each operating unit are extracted with the stacked autoencoder (SAE) to represent the essential structure of the unit. Two statistics are separately constructed using the deep features and the reconstruction error for detecting the faults in local units. Subsequently, the deep representations of the variables from each operating unit are modeled with the SAE to extract the global information for global monitoring. The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information. Therefore, the method can explain the monitoring results better. Experimental results obtained from the numerical simulation and Tennessee-Eastman process confirm the feasibility and superiority of this method. (C) 2020 Elsevier Inc. All rights reserved.