文献类型：期刊文献

英文题名：Learning Deep Correlated Representations for Nonlinear Process Monitoring

作者：Jiang, Qingchao[1];Yan, Xuefeng[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：2019

卷号：15

期号：12

起止页码：6200

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20185006242115);WOS:【SCI-EXPANDED(收录号:WOS:000502295800001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603138 and Grant 21878081, in part by Shanghai Pujiang Program under Grant 17PJD009, in part by the Fundamental Research Funds for the Central Universities under Grant 222201717006 and Grant 222201714027, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Monitoring; Feature extraction; Optimization; Correlation; Fault detection; Data mining; Kernel; Canonical correlation analysis (CCA); deep neural network (DNN); multiobjective evolutionary optimization (MEO); nonlinear process monitoring

摘要：Deep neural network (DNN) extracts hierarchical representations from process data and is promising for nonlinear process monitoring. Obtaining meaningful representations and generating efficient fault detection residual are the main challenges in DNN-based monitoring. This study proposes a regularized deep correlated representation (RDCR) method that incorporates deep belief networks (DBNs) and canonical correlation analysis (CCA) for nonlinear process monitoring. Hierarchical representations are initially extracted using DBN to process input and output variables. Second, hierarchical representations from process input and output are modeled through CCA to characterize the relationship between them. Efficient fault detection residuals are then generated, and monitoring statistics are established. CCA-based monitoring relies on the most correlated representations; thus, a multiobjective evolutionary optimization-based regularization is performed to select the most correlated representations and eliminate the influence of unrelated representations. The advantages of the RDCR monitoring are verified through experimental studies on a numerical example and the Tennessee Eastman process.