文献类型：期刊文献

英文题名：Optimized Gaussian-Process-Based Probabilistic Latent Variable Modeling Framework for Distributed Nonlinear Process Monitoring

作者：Jiang, Qingchao[1];Jiang, Jiashi[1];Zhong, Weimin[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：53

期号：5

起止页码：3187

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20225213306975);WOS:【SCI-EXPANDED(收录号:WOS:000899985400001)】；

基金：This work was supported in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61925305; in part by the National Natural Science Foundation of China under Grant 61973119; and in part by the Shanghai Rising-Star Program under Grant 20QA1402600.

语种：英文

外文关键词：Distributed process monitoring; fault detection; Gaussian process; probabilistic latent variable model

摘要：Plant-wide multiunit processes generally contain numerous variables, complex relations, and strong nonlinearity, making the monitoring of such processes challenging. This work proposes a new Gaussian-process-based probabilistic latent variable (GPPLV) modeling framework for distributed monitoring of multiunit nonlinear processes. A Gaussian-process latent variable model is first established to extract the dominant features of a local unit. Using the extracted features, a correlation between the local unit and its neighboring units are then modeled through a Gaussian-process regression (GPR) model. The genetic algorithm is used to determine the ideal independent variables from the neighboring units and optimize the hyperparameters of the GPR model simultaneously. Residuals are generated and monitoring statistics are constructed using an established GPPLV model. Experimental studies on three processes: 1) a numerical example; 2) the Tennessee Eastman benchmark process; and 3) a laboratory distillation process show that compared to some common distributed process monitoring models, the proposed method performs better in showing the nature of different faults and shows higher fault detection rate for large-scale multiunit processes.