2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] Fu Y, Wang N H, Wang Z, Wang Z Q, Ji B, Wang X C. Smelting condition identification for a fused magnesium furnace based on an acoustic signal. Journal of Materials Processing Technology, 2017, 244: 231−239 doi: 10.1016/j.jmatprotec.2016.12.017
    [2] Fu Y, Wang Z, Wang Z Q, Wang N H, Wang X C. Splattering suppression for a three-phase AC electric arc furnace in fused magnesia production based on acoustic signal. IEEE Transactions on Industrial Electronics, 2017, 64(6): 4772−4780 doi: 10.1109/TIE.2017.2668984
    [3] 吴高昌, 刘强, 柴天佑, 秦泗钊. 基于时序图像深度学习的电熔镁炉异常工况诊断. 自动化学报, 2019, 45(8): 1475−1485

    Wu Gao-Chang, Liu Qiang, Chai Tian-You, Qin Si-Zhao. Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces. Acta Automatica Sinica, 2019, 45(8): 1475−1485
    [4] 李鸿儒, 王奕文, 邓靖川. 基于信息融合的电熔镁炉熔炼异常工况等级识别. 东北大学学报 (自然科学版), 2020, 41(2): 153−157

    Li Hong-Ru, Wang Yi-Wen, Deng Jin-Chuan. Information fusion based abnormal condition levels recognition of smelting in fused magnesium furnace. Journal of Northeastern University (Natural Science), 2020, 41(2): 153−157
    [5] Zhang Y W, Fan Y P, Zhang P C. Combining kernel partial least-squares modeling and iterative learning control for the batch-to-batch optimization of constrained nonlinear processes. Industrial & Engineering Chemistry Research, 2010, 49(16): 7470−7477
    [6] Zhang Y W, Zhang P C. Optimization of nonlinear process based on sequential extreme learning machine. Chemical Engineering Science, 2011, 66(20): 4702−4710 doi: 10.1016/j.ces.2011.06.030
    [7] Zhang Y W, Ma C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chemical Engineering Science, 2011, 66(1): 64−72 doi: 10.1016/j.ces.2010.10.008
    [8] Wu Z W, Wu Y J, Chai T Y, Sun J. Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace. IEEE Transactions on Industrial Electronics, 2015, 62(3): 1703−1705 doi: 10.1109/TIE.2014.2349479
    [9] Li H, Wang F L, Li H R. Abnormal condition identification and safe control scheme for the electro-fused magnesia smelting process. ISA Transactions, 2018, 76: 178−187
    [10] 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

    Li Hui, Wang Fu-Li, Li Hong-Ru. Abnormal condition identification and self-healing control scheme for the electro-fused magnesia smelting process. Acta Automatica Sinica, 2020, 46 (7): 1411−1419
    [11] Yuan P, Sun Y F, Li H, Wang F L, Li H R. Abnormal condition identification modeling method based on Bayesian network parameters transfer learning for the electro-fused magnesia smelting process. IEEE Access, 2019, 7: 149764−149775 doi: 10.1109/ACCESS.2019.2947499
    [12] Talo M, Baloglu U B, Yıldırım Ö, Acharya U R. Application of deep transfer learning for automated brain abnormality classification using MR images. Cognitive Systems Research, 2019, 54: 176−188 doi: 10.1016/j.cogsys.2018.12.007
    [13] Cai W L, Zheng J B, Pan W K, Lin J, Li L, Chen L, et al. Neighborhood-enhanced transfer learning for one-class collaborative filtering. Neurocomputing, 2019, 341: 80−87 doi: 10.1016/j.neucom.2019.03.016
    [14] Zhang L, Liu Y, Deng P L. Odor recognition in multiple e-nose systems with cross-domain discriminative subspace learning. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1679−1692 doi: 10.1109/TIM.2017.2669818
    [15] Yang B, Lei Y G, Jia F, Xing S B. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 2019, 122: 692−706 doi: 10.1016/j.ymssp.2018.12.051
    [16] Liu X, Li Y G, Chen G X. Multimode tool tip dynamics prediction based on transfer learning. Robotics and Computer-Integrated Manufacturing, 2019, 57: 146−154 doi: 10.1016/j.rcim.2018.12.001
    [17] Chu F, Zhao X, Yao Y, Chen T, Wang F L. Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy. Journal of Process Control, 2019, 81: 197−208 doi: 10.1016/j.jprocont.2019.06.010
    [18] Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G Q. Transfer learning using computational intelligence: A survey. Knowledge-Based Systems, 2015, 80: 14−23 doi: 10.1016/j.knosys.2015.01.010
    [19] Luis R, Sucar L E, Morales E F. Inductive transfer for learning Bayesian networks. Machine Learning, 2009, 79: 227−255
    [20] Niculescu-Mizil A, Caruana R. Inductive transfer for Bayesian network structure learning. In: Proceedings of the 11th International Conference Artificial Intelligence and Statistics. San Juan, Puerto Rico, USA: AISTATS, 2007. 339−346
    [21] Oyen D, Lane T. Transfer learning for Bayesian discovery of multiple Bayesian networks. Knowledge and Information Systems, 2015, 43: 1−28 doi: 10.1007/s10115-014-0775-6
    [22] Zhou Y, Hospedales T M, Fenton N. When and where to transfer for Bayes net parameter learning. Expert Systems and Applications, 2016, 55: 361−373 doi: 10.1016/j.eswa.2016.02.011
    [23] Oyen D, Lane T. Leveraging domain knowledge in multitask Bayesian network structure learning. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence. Toronto, Canada: AAAI, 2012. 1091−1097
    [24] Zhou Y, Fenton N, Hospedales T M, Neil M. Probabilistic graphical models parameter learning with transferred prior and constraints. In: Proceedings of the 31st Conference Uncertainty in Artificial Intelligence. Amsterdam, Netherlands: AUAI, 2015. 972–981
    [25] Oyen D, Lane T. Bayesian discovery of multiple Bayesian networks via transfer learning. In: Proceedings of the 13th IEEE International Conference on Data Mining. Dallas, TX, USA: IEEE, 2013. 577–586
    [26] Fiedler L J, Sucar L E, Morales E F. Transfer learning for temporal nodes Bayesian networks. Applied Intelligence, 2015, 43: 578−597 doi: 10.1007/s10489-015-0662-1
    [27] Yang Y, Gao X G, Guo Z G, Chen D Q. Learning Bayesian networks using the constrained maximum a posteriori probability method. Pattern Recognition, 2019, 91: 123−134 doi: 10.1016/j.patcog.2019.02.006
    [28] Gao X G, Yang Y, Guo Z G. Learning Bayesian networks by constrained Bayesian estimation. Journal of Systems Engineering and Electronics, 2019, 30: 511−524 doi: 10.21629/JSEE.2019.03.09
    [29] Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques. USA: MIT Press, 2009
    [30] Yan H, Wang F L, He D K, Zhao L P, Wang Q K. Bayesian network-based modeling and operational adjustment of plantwide flotation industrial process. Industrial & Engineering Chemistry Research, 2020, 59(5): 2025−2035
    [31] Lin D K. An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning. Madison, USA: ICML, 1998. 296-304
    [32] Larrañaga P, Poza M, Yurramendi Y. Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(9): 912−926 doi: 10.1109/34.537345
    [33] Yan H, Wang F L, He D K, Wang Q K. An operational adjustment framework for a complex industrial process based on hybrid Bayesian network. IEEE Transactions on Automation Science and Engineering, 2020, 17(4): 1699−1710
  • 加载中
图(9) / 表(18)
计量
  • 文章访问数:  1460
  • HTML全文浏览量:  285
  • PDF下载量:  346
  • 被引次数: 0
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:  2021-01-29

目录

    /

    返回文章
    返回