文献类型：期刊文献

英文题名：Deep neural network based recursive feature learning for nonlinear dynamic process monitoring

作者：Zhu, Jiazhen[1];Shi, Hongbo[1];Song, Bing[1];Tan, Shuai[1];Tao, Yang[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：2020

卷号：98

期号：4

起止页码：919

外文期刊名：CANADIAN JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20195107850882);WOS:【SCI-EXPANDED(收录号:WOS:000502302800001)】；

基金：China Postdoctoral Science Foundation, Grant/Award Number: 2017M611472; Fundamental Research Funds for the Central Universities, Grant/Award Number: 222201714031; National Natural Science Foundation of China, Grant/Award Numbers: 61673173, 61703161

语种：英文

外文关键词：dynamic process; fault detection; process monitoring; stacked denoising auto-encoder

摘要：The data collected from modern industrial processes always have nonlinear and dynamic characteristics. The recently developed deep neural network method, stacked denoising auto-encoder (SDAE), can extract robust nonlinear latent variables from data against noise. However, it leaves the dynamic relationship unconsidered. To solve this problem, a novel algorithm named the recursive stacked denoising auto-encoder (RSDAE) is proposed. To learn the dynamic relationship, the RSDAE focuses on the predictability of the latent variables in the recurrence to contain the most dynamic variations. After the dynamic variations are extracted by the RSDAE, there is little autocorrelation left in the residuals. Then, the residuals can be monitored by principal component analysis (PCA). For the purpose of process monitoring, corresponding fault detection statistics are developed based on the RSDAE. Finally, a numerical case and the Tennessee Eastman process benchmark are used to demonstrate the effectiveness of the proposed algorithm.