文献类型：期刊文献

中文题名：Multimodal process monitoring based on transition-constrained Gaussian mixture model

作者：Shutian Chen[1];Qingchao Jiang[1];Xuefeng Yan[1]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2020

卷号：28

期号：12

起止页码：3070

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:【CSCD2019_2020】；

基金：supported in part by National Natural Science Foundation of China under Grants 61973119 and 61603138;in part by Shanghai Rising-Star Program under Grant 20QA1402600;in part by the Open Funding from Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems under Grant SKDN202001;in part by the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017.

语种：英文

中文关键词：Multimodal process monitoring;Gaussian mixture model;State transition matrix;Process control;Process systems;Systems engineering

摘要：Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.