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电熔镁炉熔炼过程异常工况识别及自愈控制方法

李荟 王福利 李鸿儒

李荟, 王福利, 李鸿儒. 电熔镁炉熔炼过程异常工况识别及自愈控制方法. 自动化学报, 2020, 46(7): 1411-1419. doi: 10.16383/j.aas.2018.c180333
引用本文: 李荟, 王福利, 李鸿儒. 电熔镁炉熔炼过程异常工况识别及自愈控制方法. 自动化学报, 2020, 46(7): 1411-1419. doi: 10.16383/j.aas.2018.c180333
LI Hui, WANG Fu-Li, LI Hong-Ru. Abnormal Condition Identiflcation and Self-Healing Control Scheme for the Electro-Fused Magnesia Smelting Process. ACTA AUTOMATICA SINICA, 2020, 46(7): 1411-1419. doi: 10.16383/j.aas.2018.c180333
Citation: LI Hui, WANG Fu-Li, LI Hong-Ru. Abnormal Condition Identiflcation and Self-Healing Control Scheme for the Electro-Fused Magnesia Smelting Process. ACTA AUTOMATICA SINICA, 2020, 46(7): 1411-1419. doi: 10.16383/j.aas.2018.c180333

电熔镁炉熔炼过程异常工况识别及自愈控制方法

doi: 10.16383/j.aas.2018.c180333
基金项目: 

国家自然科学基金 61533007

国家自然科学基金 61873049

国家重点研发计划 2017YFB0304205

详细信息
    作者简介:

    李荟  东北大学信息科学与工程学院博士研究生.主要研究方向为复杂工业过程异常工况识别及自愈控制, 人工智能方法及应用. E-mail: lihui_neu@163.com

    李鸿儒  东北大学信息科学与工程学院教授.主要研究方向为复杂工业过程建模、控制与优化, 人工智能方法及应用, 工业过程监测及健康维护. E-mail: lihongru@ise.neu.edu.cn

    通讯作者:

    王福利  东北大学信息科学与工程学院教授.主要研究方向为复杂工业过程建模与优化、故障诊断.本文通信作者. E-mail: wangfuli@ise.neu.edu.cn

Abnormal Condition Identiflcation and Self-Healing Control Scheme for the Electro-Fused Magnesia Smelting Process

Funds: 

National Natural Science Foundation of China 61533007

National Natural Science Foundation of China 61873049

National Key Research and Development Program of China 2017YFB0304205

More Information
    Author Bio:

    LI Hui Ph.D. candidate at the College of Information Science and Engineering, Northeastern University. Her research interest covers abnormal condition identification and self-healing control for complex industrial system, artificial intelligence and its applications

    LI Hong-Ru Professor at the College of Information Science and Engineering, Northeastern University. His research interest covers modeling, control and optimization for complex systems, artificial intelligence and its applications, monitoring and health maintenance for industrial system

    Corresponding author: WANG Fu-Li Professor at the College of Information Science and Engineering, Northeastern University. His research interest covers modeling and optimization for complex system, and fault diagnosis. Corresponding author of this paper
  • 摘要:

    本文提出了基于多源信息融合的电熔镁炉异常工况识别及自愈控制方法.通过分析与三种异常相关的专家知识及操作经验, 本文提取了与异常工况相关的多源信息.通过融合多源信息, 建立了用于异常工况识别的贝叶斯网络模型.根据异常工况的识别结果, 利用剩余生命时间与控制变量调整量间的关系获得自愈控制措施.仿真结果表明提出的方法能够实现异常工况识别, 并且能够区分严重程度, 制定相应的自愈控制方案, 获得比现有方法更好的性能.

    Recommended by Associate Editor FU Jun
    1)  本文责任编委 付俊
  • 图  1  电熔镁炉熔炼过程

    Fig.  1  The electro-fused magnesia furnace smelting process

    图  2  用于识别半熔化异常工况的贝叶斯网络模型

    Fig.  2  The established Bayesian network for the semimolten condition

    图  3  用于识别过加热异常工况的贝叶斯网络模型

    Fig.  3  The established Bayesian network for the overheating condition

    图  4  用于识别排气异常工况的贝叶斯网络模型

    Fig.  4  The established Bayesian network for the abnormal exhausting condition

    图  5  生命周期曲线示意图

    Fig.  5  The schematic diagram of life cycle

    图  6  电熔镁炉熔炼过程仿真平台

    Fig.  6  The simulation platform for the electro-fused magnesia smelting process

    图  7  半熔化轻微异常工况的自愈控制效果

    Fig.  7  The self-healing control effect for the slight semimolten condition

    图  8  过加热轻微异常工况的自愈控制效果

    Fig.  8  The self-healing control effect for the slight overheating condition

    图  9  排气轻微异常工况的自愈控制效果

    Fig.  9  The self-healing control effect for the slight abnormal exhausting condition

    表  1  半熔化异常工况的典型事件

    Table  1  The typical scenarios for the semimolten condition

    事件编号 $A_1$ $B_1$ $C_1$ $D_1$ $E_1$
    1 1 1 1 1 1
    2 1 1 1 3 1
    3 1 1 1 2 2
    4 1 1 1 2 3
    5 2 2 2 2 3
    6 2 2 2 2 4
    7 3 3 3 2 3
    8 3 3 3 2 4
    下载: 导出CSV

    表  2  过加热异常工况的典型事件

    Table  2  The typical scenarios for the overheating condition

    事件编号 $A_2$ $B_2$ $C_2$ $D_2$
    1 1 1 2 1
    2 1 1 2 2
    3 1 1 3 1
    4 1 1 3 2
    5 1 1 1 3
    6 1 1 1 4
    7 2 2 1 3
    8 2 2 1 4
    9 3 3 1 3
    10 3 3 1 4
    下载: 导出CSV

    表  3  排气异常工况的典型事件

    Table  3  The typical scenarios for the abnormal exhausting condition

    事件编号 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
    $A_3$ 1 2 2 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1
    $B_3$ 1 2 3 2 3 3 2 3 1 1 1 1 1 1 1 1 1 1
    $C_3$ 1 1 1 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2
    $D_3$ 1 1 1 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2
    $E_3$ 1 1 1 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2
    $F_3$ 1 1 1 1 1 2 2 2 3 3 2 3 2 3 2 2 3 3
    $G_3$ 1 1 1 1 1 2 2 2 2 4 3 3 4 2 3 4 3 4
    下载: 导出CSV

    表  4  针对表 1的识别结果

    Table  4  The identification results for the Table 1

    事件编号 1~2 3~5 6 7~8
    辨识结果 1 2 3 4
    下载: 导出CSV

    表  5  针对表 2中的识别结果

    Table  5  The identification results for the Table 2

    事件编号 1~4 5~7 8 9~10
    辨识结果 1 2 3 4
    下载: 导出CSV

    表  6  针对表 3的识别结果

    Table  6  The identification results for the Table 3

    事件编号 1 2~5 6~8 9~18
    辨识结果 1 2 3 4
    下载: 导出CSV

    表  7  半熔化异常工况识别结果对比

    Table  7  The identification results comparison for the semimolten condition

    证据事件编号 3 4 5 6 7 8
    正常 0.2654 0.2449 0.0009 0.0009 0.0002 0.0001
    轻微异常 0.6032 0.3379 0.4639 0.1827 0.182 0.0438
    中度异常 0.1073 0.2949 0.4565 0.6371 0.3401 0.2904
    严重异常 0.0241 0.1222 0.0787 0.1793 0.4776 0.6656
    下载: 导出CSV

    表  8  过加热异常工况识别结果对比

    Table  8  The identification results comparison for the overheating condition

    证据事件编号 5 6 7 8 9 10
    正常 0.13 0.1134 0.001 0.0008 0.0003 0.0002
    轻微异常 0.475 0.37 0.5233 0.3935 0.2676 0.1698
    中度异常 0.2931 0.3617 0.3347 0.3988 0.3274 0.3291
    严重异常 0.1019 0.1548 0.141 0.2069 0.4047 0.5009
    下载: 导出CSV

    表  9  排气异常工况识别结果对比

    Table  9  The identification results comparison for the abnormal exhausting condition

    事件编号 1 2~5 6~8 9~18
    本文方法辨识结果 1 2 3 4
    传统方法辨识结果 1 1 1 4
    下载: 导出CSV
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  • 收稿日期:  2018-05-22
  • 录用日期:  2018-10-09
  • 刊出日期:  2020-07-24

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