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个人信息Personal Information
教授
博士生导师
硕士生导师
主要任职：Professor at the Institute of Advanced Measurement & Control Technology
其他任职：先进检测与控制技术研究所所长
性别：男
毕业院校：上海交通大学
学位：博士
所在单位：控制科学与工程学院
学科：控制理论与控制工程. 化学工程
办公地点：大连理工大学控制科学与工程学院先进检测与控制技术研究所
大连市凌工路2号大连理工大学海山楼A座724室
联系方式：Tel:(0411)84706465 实验室网站：http://act.dlut.edu.cn/
电子邮箱：tliu@dlut.edu.cn
Sequential local-based Gaussian mixture model for monitoring multiphase batch processes
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论文类型：期刊论文
发表时间：2018-05-18
发表刊物：CHEMICAL ENGINEERING SCIENCE
收录刊物：SCIE、EI
卷号：181
页面范围：101-113
ISSN号：0009-2509
关键字：Multiphase batch process; Multivariate statistical process monitoring; Sequential phase partition; Gaussian mixture model; Fault detection
摘要：To address the incapability of using a single model to monitor multiphase batch processes with varying characteristics in different phases, a sequential local-based Gaussian mixture model (GMM) building method is proposed in this paper to improve monitoring performance. A multiphase process is divided into stable phases and transition phases in terms of the time sequence for sampling. Samples in local regions are used to partition each phase via two types of models, initial model and mixture model. Meanwhile, an adaptive iteration strategy is developed to properly determine stable phases including the in-between transition phases. Based on the partitioned phases, a localized probability index is introduced for process monitoring. A numerical example and a fed-batch penicillin fermentation process are used to demonstrate the effectiveness and merit of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.