文献类型：期刊文献

英文题名：Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring

作者：Jiang, Qingchao[1];Yan, Shifu[1];Yan, Xuefeng[1];Yi, Hui[2];Gao, Furong[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Nanjing Univ Technol, Coll Elect Engn & Control Sci, Nanjing 211816, Peoples R China;[3]Hong Kong Univ Sci & Technol, Dept Chem & Biomol Engn, Clear Water Bay, Hong Kong 999077, Peoples R China

年份：2020

卷号：16

期号：4

起止页码：2839

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20200508114931);WOS:【SCI-EXPANDED(收录号:WOS:000510901000067)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603138 and Grant 61973119, in part by Shanghai Pujiang Program under Grant 17PJD009, in part by the Fundamental Research Funds for the Central Universities under Grant 222201917006, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, under Grant ICT1900331.

语种：英文

外文关键词：Batch process monitoring; data-driven fault detection; deep correlated representation learning (DCRL); two-dimensional (2-D) modeling

摘要：Dynamics and nonlinearity may exist in the time and batch directions for batch processes, thereby complicating the monitoring of these processes. In this article, we propose a two-dimensional deep correlated representation learning (2D-DCRL) method to achieve the efficient fault detection and isolation of the nonlinear batch processes. Three-way historical data are first unfolded as two-way time-slice data. Second, a stacked autoencoder based deep neural network is constructed to characterize the correlation among the process variables. Considering that the time and batch directions may be dynamic, for each time-slice measurement, a constructed 2-D measurement containing samples from the previous time instants and batches is then obtained. Subsequently, DCRL is performed between the current running-batch measurements and the constructed 2-D measurements to characterize the 2-D dynamics and nonlinearity. The 2D-DCRL-based monitoring examines the status of a sample by considering the 2-D nonlinear and dynamic information, providing improved monitoring performance. Applications on two typical batch processes demonstrate the effectiveness of the proposed 2D-DCRL monitoring scheme.