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过程工业大数据建模研究展望

刘强 秦泗钊

刘强, 秦泗钊. 过程工业大数据建模研究展望. 自动化学报, 2016, 42(2): 161-171. doi: 10.16383/j.aas.2016.c150510
引用本文: 刘强, 秦泗钊. 过程工业大数据建模研究展望. 自动化学报, 2016, 42(2): 161-171. doi: 10.16383/j.aas.2016.c150510
LIU Qiang, QIN S. Joe. Perspectives on Big Data Modeling of Process Industries. ACTA AUTOMATICA SINICA, 2016, 42(2): 161-171. doi: 10.16383/j.aas.2016.c150510
Citation: LIU Qiang, QIN S. Joe. Perspectives on Big Data Modeling of Process Industries. ACTA AUTOMATICA SINICA, 2016, 42(2): 161-171. doi: 10.16383/j.aas.2016.c150510

过程工业大数据建模研究展望

doi: 10.16383/j.aas.2016.c150510
基金项目: 

中国博士后科学基金 2013M541242

博士后国际交流计划派出项目 20130020

中央高校基本科研业务费 N130108001

国家自然科学基金 61203102

国家自然科学基金 61490704

中央高校基本科研业务费 N130408002

国家自然科学基金 61304107

国家自然科学基金 61290323

国家自然科学基金 61573022

详细信息
    作者简介:

    刘强 东北大学流程工业综合自动化国家重点实验室讲师, 美国南加州大学化工系博士后.主要研究方向为基于数据的复杂工业过程建模与故障诊断.E-mail:liuq@mail.neu.edu.cn

    通讯作者:

    秦泗钊 香港中文大学 (深圳) 教授, IEEE会士、IFAC会士.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.本文通信作者.E-mail:joeqin@cuhk.edu.cn

Perspectives on Big Data Modeling of Process Industries

Funds: 

the China Postdoctoral Science Foundation 2013M541242

the International Postdoctoral Exchange Fellowship Program 20130020

the Fundamental Research Funds for the Central Universities N130108001

Supported by National Natural Science Foundation of China 61203102

Supported by National Natural Science Foundation of China 61490704

the Fundamental Research Funds for the Central Universities N130408002

Supported by National Natural Science Foundation of China 61304107

Supported by National Natural Science Foundation of China 61290323

Supported by National Natural Science Foundation of China 61573022

More Information
    Author Bio:

    Lecturer at the State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), China, and Postdoctor at the Department of Chemical Engineering, University of Southern California, USA. His research interest covers statistical process monitoring, fault diagnosis of complex industrial processes

    Corresponding author: QIN S. Joe Professor at the Chinese University of Hong Kong, Shenzhen, China. He is a Fellow of the International Federation of Automatic Control and a Fellow of IEEE. His research interest covers statistical process monitoring, fault diagnosis, model predictive control, system identification, building energy optimization, and control performance monitoring. Corresponding author of this paper
  • 摘要: 人们对大数据的认识已从"3Vs" (Volume-大容量; Variety-多样性; Velocity-处理实时性)、"4Vs" ("3Vs"与Value-价值)、到现今的"5Vs" ("4Vs"与Veracity-真实性).在此背景下, 首先分析过程工业大数据的"5Vs"特性; 接下来, 综述现有数据建模方法, 并结合过程工业大数据特有性质 (包括:多层面不规则采样性、多时空时间序列性、不真实数据混杂性) 论述现有数据建模方法应用于工业大数据建模时的局限; 最后, 探讨过程工业大数据建模有待研究的问题, 包括:1) 多层面不规则采样数据的潜结构建模; 2) 用于事件发现、决策和因果分析的多时空时间序列数据建模; 3) 含有不真实数据的鲁棒建模; 4) 支持实时建模的大容量数据计算架构与方法.
  • 图  1  过程工业多层面、不规则采样时间序列数据

    Fig.  1  Multi-layer irregularly sampling time-series data of process industries

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  • 收稿日期:  2015-08-13
  • 录用日期:  2015-10-23
  • 刊出日期:  2016-02-20

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