文献类型：期刊文献

中文题名：Distributed process monitoring based on Kantorovich distancemultiblock variational autoencoder and Bayesian inference

作者：Zongyu Yao[1];Qingchao Jiang[1];Xingsheng Gu[1]

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2024

卷号：73

期号：9

起止页码：311

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；PubMed;

基金：support from the National Key Research&Development Program of China(2021YFC2101100);the National Natural Science Foundation of China(62322309,61973119).

语种：英文

中文关键词：Chemical processes;Safety;Kantorovich distance;Neural networks;Process monitoring;Bayesian inference

摘要：Modern industrial processes are typically characterized by large-scale and intricate internal relationships.Therefore,the distributed modeling process monitoring method is effective.A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder(KD-MBVAE)is introduced.Firstly,given the high consistency of relevant variables within each sub-block during the change process,the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory.Subsequently,the variational autoencoder(VAE)model was separately established,and corresponding T^(2)statistics were calculated.To improve fault sensitivity further,a novel statistic,derived from Kantorovich distance,is introduced by analyzing model residuals from the perspective of probability distribution.The thresholds of both statistics were determined by kernel density estimation.Finally,monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference.Additionally,a novel approach for fault diagnosis is introduced.The feasibility and efficiency of the introduced scheme are verified through two cases.