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数据驱动的工业过程运行监控与自优化研究展望

刘强 卓洁 郎自强 秦泗钊

刘强, 卓洁, 郎自强, 秦泗钊. 数据驱动的工业过程运行监控与自优化研究展望. 自动化学报, 2018, 44(11): 1944-1956. doi: 10.16383/j.aas.2018.c180207
引用本文: 刘强, 卓洁, 郎自强, 秦泗钊. 数据驱动的工业过程运行监控与自优化研究展望. 自动化学报, 2018, 44(11): 1944-1956. doi: 10.16383/j.aas.2018.c180207
LIU Qiang, ZHUO Jie, LANG Zi-Qiang, QIN S. Joe. Perspectives on Data-driven Operation Monitoring and Self-optimization of Industrial Processes. ACTA AUTOMATICA SINICA, 2018, 44(11): 1944-1956. doi: 10.16383/j.aas.2018.c180207
Citation: LIU Qiang, ZHUO Jie, LANG Zi-Qiang, QIN S. Joe. Perspectives on Data-driven Operation Monitoring and Self-optimization of Industrial Processes. ACTA AUTOMATICA SINICA, 2018, 44(11): 1944-1956. doi: 10.16383/j.aas.2018.c180207

数据驱动的工业过程运行监控与自优化研究展望

doi: 10.16383/j.aas.2018.c180207
基金项目: 

国家自然科学基金 61573022

国家自然科学基金 61490701

国家自然科学基金 61673097

中央高校基本科研业务费 N160804002

国家自然科学基金 61490704

中央高校基本科研业务费 N160801001

详细信息
    作者简介:

    卓洁  东北大学硕士研究生.主要研究方向为工业过程统计过程监控与故障诊断.E-mail:zhuojie1996@126.com

    郎自强  英国谢菲尔德大学教授.主要研究方向为非线性系统建模, 分析和设计, 工程系统健康监测与故障诊断.E-mail:z.lang@sheffield.ac.uk

    秦泗钊  美国南加州大学教授, IEEE Fellow, IFACFellow, AIChE Fellow.主要研究方向为统计过程监控, 故障诊断, 模型预测控制, 系统辨识, 建筑能源优化与控制性能监控.E-mail:sqin@usc.edu

    通讯作者:

    刘强东北大学副教授.2014~2016年间于美国南加州大学化工系从事博士后研究.主要研究方向为数据驱动的建模, 过程监控与故障诊断.本文通信作者.E-mail:liuq@mail.neu.edu.cn

Perspectives on Data-driven Operation Monitoring and Self-optimization of Industrial Processes

Funds: 

National Natural Science Foundation of China 61573022

National Natural Science Foundation of China 61490701

National Natural Science Foundation of China 61673097

the Fundamental Research Funds for the Central Universities N160804002

National Natural Science Foundation of China 61490704

the Fundamental Research Funds for the Central Universities N160801001

More Information
    Author Bio:

      Master student at Northeastern University, China. Her research interest covers statistical process monitoring, fault diagnosis of complex industrial processes

      Professor at the University of Sheffield, UK. His research interest covers nonlinear system modeling, analysis and design, health monitoring and fault diagnosis of engineering system

      Professor at the University of Southern California, USA. IEEE Fellow, IFAC Fellow and AIChE Fellow. His research interest covers statistical process monitoring, fault diagnosis, model predictive control, system identification, building energy optimization, and control performance monitoring

    Corresponding author: LIU Qiang  Associate professor at Northeastern University, China. He was a Research Fellow at University of Southern California during the year of 2014 to 2016. His research interest covers data-driven process modeling, monitoring, and fault diagnosis of complex industrial processes. Corresponding author of this paper
  • 摘要: 现代工业过程向大规模、连续化、集成化方向发展,有必要对生产全流程运行的决策、协同控制、底层控制进行有效监控,也是当前国际控制领域的研究热点.本文首先分析了工业过程全流程运行监控的内涵与行业现状;其次,阐述了基于模型的控制系统故障诊断与容错控制方法,以及数据驱动的异常工况诊断与自愈控制方法的研究现状,并指明了信息物理系统(Cyber-physical systems,CPS)时代智能安全运行监控与自优化的发展机遇;最后,论述了工业过程运行监控与自优化研究的新方向和最新进展,包括:1)数据驱动的决策、协同控制、底层控制多层面联合监控;2)基于机理、数据、知识多源动态信息融合的异常工况诊断;3)专家知识与控制手段相结合的协同层自愈控制;4)数据驱动的运行动态性能分析与自优化;5)支撑运行监控与自优化系统的实现技术.
    1)  本文责任编委 谢永芳
  • 图  1  工业过程运行监控系统的作用

    Fig.  1  Role of operational monitoring system

    图  2  复杂工业过程智能化安全运行监控与自优化系统的架构

    Fig.  2  Intelligent operational monitoring and self-optimization of complex industrial processes

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  • 收稿日期:  2018-04-10
  • 录用日期:  2018-07-23
  • 刊出日期:  2018-11-20

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