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基于贝叶斯网络参数迁移学习的电熔镁炉异常工况识别

闫浩 王福利 孙钰沣 何大阔

闫浩, 王福利, 孙钰沣, 何大阔. 基于贝叶斯网络参数迁移学习的电熔镁炉异常工况识别. 自动化学报, 2021, 47(1): 197−208 doi: 10.16383/j.aas.c200104
引用本文: 闫浩, 王福利, 孙钰沣, 何大阔. 基于贝叶斯网络参数迁移学习的电熔镁炉异常工况识别. 自动化学报, 2021, 47(1): 197−208 doi: 10.16383/j.aas.c200104
Yan Hao, Wang Fu-Li, Sun Yu-Feng, He Da-Kuo. Abnormal condition identification based on Bayesian network parameter transfer learning for the electro-fused magnesia. Acta Automatica Sinica, 2021, 47(1): 197−208 doi: 10.16383/j.aas.c200104
Citation: Yan Hao, Wang Fu-Li, Sun Yu-Feng, He Da-Kuo. Abnormal condition identification based on Bayesian network parameter transfer learning for the electro-fused magnesia. Acta Automatica Sinica, 2021, 47(1): 197−208 doi: 10.16383/j.aas.c200104

基于贝叶斯网络参数迁移学习的电熔镁炉异常工况识别

doi: 10.16383/j.aas.c200104
基金项目: 国家自然科学基金(61973057, 61533007, 61773105, 61873053), 创新研究群体科学基金(61621004), 中央高校基本科研业务费(N182008004) 资助
详细信息
    作者简介:

    闫浩:东北大学信息科学与工程学院博士研究生. 主要研究方向为人工智能, 复杂工业过程智能建模、控制与优化, 数据挖掘.E-mail: 13644971979@163.com

    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

    孙钰沣:东北大学信息科学与工程学院硕士研究生. 主要研究方向为迁移学习, 贝叶斯网络, 人工智能, 故障诊断. E-mail: sunyufeng0411@163.com

    何大阔:东北大学信息科学与工程学院教授. 主要研究方向为智能建模、控制与优化, 复杂工业生产全流程智能监测与故障诊断技术.E-mail: hedakuo@ise.neu.edu.cn

Abnormal Condition Identification Based on Bayesian Network Parameter Transfer Learning for the Electro-fused Magnesia

Funds: Supported by National Natural Science Foundation of China (61973057, 61533007, 61773105, 61873053), the Innovative Research Groups of the National Natural Science Foundation of China (61621004), the Fundamental Research Funds for the Central Universities (N182008004)
  • 摘要:

    在贝叶斯网络(Bayesian network, BN)参数学习中, 如果数据不够充分, 将无法建立准确的BN模型来分析和解决问题. 针对电熔镁炉熔炼过程的异常工况识别建模, 提出一种新的BN参数迁移学习方法来改进异常工况识别精度. 该方法可以解决源域BN与目标域BN在结构不一致情况下的参数迁移学习问题. 在实验部分, 首先在著名的Asia网络上对该方法进行了验证, 然后将其应用于电熔镁炉熔炼过程排气异常工况识别BN模型的参数学习. 实验结果表明, 与小数据下建立的目标域BN模型相比, 该方法较大地提高了异常工况识别的准确性.

  • 图  1  BN分解示意图

    Fig.  1  Schematic diagram of BN decomposition

    图  2  电熔镁炉熔炼过程示意图

    Fig.  2  Schematic diagram of the electro-fused magnesia furnace smelting process

    图  3  电熔镁炉排气异常工况的BN模型结构

    Fig.  3  BN model structure for the abnormal exhausting condition of the electro-fused magnesia furnace

    图  4  提出的BN参数迁移学习方法流程图

    Fig.  4  Flow diagram of the proposed BN parameter transfer learning method

    图  5  确定备选源域示意图

    Fig.  5  Schematic diagram for determining alternative source domain

    图  6  Asia网络结构

    Fig.  6  Structure of the Asia network

    图  7  相关的源域BN

    Fig.  7  Related source domain BN

    图  8  不同权重$\eta $下的KL散度值

    Fig.  8  The values of KL divergence under the different weights $\eta $

    图  9  电熔镁炉仿真平台

    Fig.  9  Construction of simulation platform for electric-fused magnesium furnace

    表  1  各节点物理意义

    Table  1  Physical meaning of the nodes

    节点物理意义
    A排气异常工况
    B异常声音信息
    C异常图像信息
    D异常电流信息
    E在飞溅特征频率下的短时能量
    F在飞溅特征频率下的幅值
    G平均灰度
    H灰度方差
    I灰度丰度
    J电流变化率
    K电流跟踪误差
    下载: 导出CSV

    表  2  节点$\underline{\underline {\rm A}} $$\underline{\underline {\rm S}} $的CPTs

    Table  2  The CPTs of nodes $\underline{\underline {\rm A}}$ and $\underline{\underline {\rm S}} $

    $\underline{\underline {\rm A}} $ (i = 1)$\underline{\underline {\rm S}} $ (i = 2)
    0 (k = 1)0.990.5
    1 (k = 2)0.010.5
    下载: 导出CSV

    表  3  节点$\underline{\underline {\rm T}} $的CPT

    Table  3  The CPT of node $\underline{\underline {\rm T}} $

    $\underline{\underline {\rm A}} $
    0 (j = 1)1 (j = 2)
    $\underline{\underline {\rm T}} $ (i = 3)0 (k = 1)0.990.95
    1 (k = 2)0.010.05
    下载: 导出CSV

    表  4  节点$\underline{\underline {\rm L}} $的CPT

    Table  4  The CPT of node $\underline{\underline {\rm L}} $

    $\underline{\underline {\rm S}} $
    0 (j = 1)1 (j = 2)
    $\underline{\underline {\rm L}} $ (i = 4)0 (k = 1)0.990.9
    1 (k = 2)0.010.1
    下载: 导出CSV

    表  5  节点$\underline{\underline {\rm B}}$的CPT

    Table  5  The CPT of node $\underline{\underline {\rm B}} $

    $\underline{\underline {\rm S}} $
    0 (j = 1)1 (j = 2)
    $\underline{\underline {\rm B}} $ (i = 5)0 (k = 1)0.990.9
    1 (k = 2)0.010.1
    下载: 导出CSV

    表  6  节点$\underline{\underline {\rm E}} $的CPT

    Table  6  The CPT of node $\underline{\underline {\rm E}} $

    $\underline{\underline {\rm T}} $01
    $\underline{\underline {\rm L}} $0 (j = 1)1 (j = 2)0 (j = 3)1 (j = 4)
    $\underline{\underline {\rm E}} $ (i = 6)0 (k = 1)1000
    1 (k = 2)0111
    下载: 导出CSV

    表  7  节点$\underline{\underline {\rm X}} $的CPT

    Table  7  The CPT of node $\underline{\underline {\rm X}}$

    $\underline{\underline {\rm E}} $
    0 (j = 1)1 (j = 2)
    $\underline{\underline {\rm X}} $ (i = 7)0 (k = 1)0.950.02
    1 (k = 2)0.050.98
    下载: 导出CSV

    表  8  节点$\underline{\underline {\rm D}} $的CPT

    Table  8  The CPT of node $\underline{\underline {\rm D}} $

    $\underline{\underline {\rm E}} $01
    $\underline{\underline {\rm B}} $0 (j = 1)1 (j = 2)0 (j = 3)1 (j = 4)
    $\underline{\underline {\rm D}} $ (i = 8)0 (k = 1)0.90.20.30.1
    1 (k = 2)0.10.80.70.9
    下载: 导出CSV

    表  9  Asia网络专家知识形式二

    Table  9  The expert knowledge form two for the Asia network

    序号约束得分
    1$\theta _{321}^t > 0.8$1
    2$0.85 < \theta _{421}^t < 0.95$1
    3$0.35 < \theta _{521}^t < 0.45$1
    4$0.85 < \theta _{811}^t < 0.95$1
    5$\theta _{321}^t > 0.9$2
    6$0.88 < \theta _{421}^t < 0.92$2
    7$0.38 < \theta _{521}^t < 0.42$2
    8$0.88 < \theta _{811}^t < 0.92$2
    下载: 导出CSV

    表  10  节点A的参数表达形式

    Table  10  The parameters expression of node A

    A (i = 1)
    1 (k = 1)$\theta _{1\_1}^t$
    2 (k = 2)$\theta _{1\_2}^t$
    3 (k = 3)$\theta _{1\_3}^t$
    4 (k = 4)$\theta _{1\_4}^t$
    下载: 导出CSV

    表  11  节点B的参数表达形式

    Table  11  The parameters expression of node B

    A (i = 1)
    1 (j = 1)2 (j = 2)3 (j = 3)4 (j = 4)
    1 (k = 1)$\theta _{211}^t$$\theta _{221}^t$$\theta _{231}^t$$\theta _{241}^t$
    B (i = 2)2 (k = 2)$\theta _{212}^t$$\theta _{222}^t$$\theta _{232}^t$$\theta _{242}^t$
    3 (k = 3)$\theta _{213}^t$$\theta _{223}^t$$\theta _{233}^t$$\theta _{243}^t$
    下载: 导出CSV

    表  12  排气异常工况识别模型的专家知识形式一

    Table  12  The expert knowledge form one for the abnormal exhausting condition model

    序号约束
    1$\theta _{232}^t < \theta _{233}^t$
    2$\theta _{333}^t < \theta _{332}^t$
    3$\theta _{434}^t < \theta _{433}^t$
    4$\theta _{521}^t < \theta _{522}^t$
    5$\theta _{831}^t < \theta _{833}^t$
    6$\theta _{1031}^t < \theta _{1032}^t$
    下载: 导出CSV

    表  13  排气异常工况识别模型的专家知识形式二

    Table  13  The expert knowledge form two for the abnormal exhausting condition model

    序号约束得分
    1$0.03 < \theta _{341}^t < 0.07$1
    2$0.01 < \theta _{512}^t < 0.05$1
    3$0.13 < \theta _{923}^t < 0.17$1
    4$0.78 < \theta _{1122}^t < 0.82$1
    5$0.04 < \theta _{341}^t < 0.06$2
    6$0.02 < \theta _{512}^t < 0.04$2
    7$0.14 < \theta _{923}^t < 0.16$2
    8$0.79 < \theta _{1122}^t < 0.81$2
    下载: 导出CSV

    表  14  三种模型描述

    Table  14  Descriptions of the three models

    模型建模方法描述
    模型一仅使用目标域的稀缺数据学习目标域 BN 模型参数
    模型二使用本文提出的方法学习目标域 BN 模型参数
    模型三只考虑结构一致下的相关源域来辅助学习目标域 BN 模型参数
    下载: 导出CSV

    表  15  排气异常工况的典型事件

    Table  15  The typical scenarios for the abnormal exhausting condition

    编号123456789
    E122333321
    F123233231
    G111111113
    H111111113
    I111111113
    J111112223
    K111112222
    编号101112131415161718
    E111111111
    F111111111
    G333322222
    H333322222
    I333322222
    J323232233
    K433423434
    下载: 导出CSV

    表  16  排气异常工况识别模型一的识别结果

    Table  16  The identification results of abnormal scenarios for model one

    事件编号123456789
    节点A的状态10.51670.38870.37300.37210.35720.31230.29640.28490.0026
    20.24710.35540.40890.43070.48650.25470.20550.18710.0182
    30.23410.25460.21700.19620.15550.42430.48780.51740.5426
    40.00210.00130.00110.00100.00080.00870.01030.01060.4366
    事件编号101112131415161718
    节点A的状态10.00170.00090.00090.00420.12080.04290.22680.04020.0815
    20.02750.04570.03140.02770.02820.07840.05150.04830.0433
    30.52410.55300.47650.71570.14900.16770.23540.12950.1461
    40.44670.40040.49120.25240.70200.71100.48630.78200.7291
    下载: 导出CSV

    表  17  排气异常工况识别模型二的识别结果

    Table  17  The identification results of abnormal scenarios for model two

    事件编号123456789
    节点A的状态10.84810.04950.11050.12870.17720.04330.03940.03420.0075
    20.12660.87390.75890.73770.63440.18950.27610.28780.0079
    30.02340.07460.12930.13230.18770.76290.67380.66680.1440
    40.00190.00200.00130.00130.00070.00430.01070.01120.8406
    事件编号101112131415161718
    节点A的状态10.00180.00170.00120.00390.02450.00590.01300.00390.0060
    20.00460.00410.00410.00580.04210.02230.03130.02270.0257
    30.07740.07930.07130.10140.15230.08730.10980.07860.0851
    40.91620.91490.92340.88890.78110.88450.84590.89480.8832
    下载: 导出CSV

    表  18  排气异常工况识别模型三的识别结果

    Table  18  The identification results of abnormal scenarios for model three

    事件编号123456789
    节点A的状态10.79870.04120.13680.13620.12590.04210.00490.00520.0021
    20.18810.82750.75700.74980.64110.19220.35740.37570.0072
    30.00760.12810.10340.11120.23090.75930.62400.60460.1293
    40.00560.00320.00280.00280.00210.00640.01370.01450.8614
    事件编号101112131415161718
    节点A的状态10.00030.00030.00020.00060.01720.00310.00630.00200.0031
    20.00140.00160.00110.10260.10150.02780.04290.01960.0250
    30.11420.11680.11370.57820.24610.17350.18520.14730.1504
    40.88410.88130.88500.31860.63520.79560.76560.83110.8215
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-04
  • 录用日期:  2020-05-07
  • 网络出版日期:  2021-01-29
  • 刊出日期:  2021-01-20

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