文献类型：期刊文献

英文题名：No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models

作者：Cao, Yue[1,2,3];Jan, Nabil Magbool[4];Huang, Biao[2];Wang, Yalin[3];Pan, Zhuofu[3];Gui, Weihua[3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 1H9, Canada;[3]Cent South Univ, Sch Automat, Changsha 410083, Peoples R China;[4]Indian Inst Technol Tirupati, Dept Chem Engn, Tirupati 517506, Andhra Pradesh, India

年份：2023

卷号：20

期号：1

起止页码：167

外文期刊名：IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

收录：;EI(收录号：20220611601827);WOS:【SCI-EXPANDED(收录号:WOS:000751498300001)】；

基金：This work was supported in part by the Major Program of National Natural Science Foundation of China (NSFC) under Grant 61590921, in part by the "Overseas Expertise Introduction Project for Discipline Innovation" 111 Project under Grant B17048, in part by the Projects of International Cooperation and Exchanges NSFC under Grant 61860206014, in part by the Program of China Scholarship Council under Grant 201806370151, and in part by the Fundamental Research Funds for Central Universities of Central South University under Grant 2017zzts135.

语种：英文

外文关键词：Process monitoring; Principal component analysis; Bayes methods; Probabilistic logic; Delay effects; Sensitivity; Gaussian distribution; Kullback-Leibler divergence; Gaussian mixture model; variational Bayesian PCA; no-delay multimodal process monitoring

摘要：The primary goal of multimodal process monitoring is to detect abnormalities or occurrence of faults. However, the profound challenge in the multimodal monitoring problem is that it is difficult to quickly distinguish the fault occurrence on a process mode from other operating modes. In this work, a Gaussian mixture model based variational Bayesian principal component analysis (GMM-VBPCA) is proposed. GMM is used to capture the global multimodal information where each Gaussian component of GMM represents a corresponding normal operating mode. VBPCA is employed to construct a probabilistic model for each operating mode. Using the weights of posterior probabilities from global GMM, local VBPCA models can then be fused to characterize the normal multimodal processes. In order to detect the occurrence of faults, Kullback-Leibler (KL) divergence of latents and model residuals of the multimodal process are used as the monitoring statistics that measure the deviation from the normal multimodal distribution. Owing to the variational local model, the posterior distribution of latents and model residuals of the GMM-VBPCA can characterize the process behavior for every test sample. Finally, GMM-VBPCA based monitoring statistics are compared with existing process monitoring methods through a simulated numerical example and an industrial hydrocracking process.