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基于多模态特征子集选择性集成建模的磨机负荷参数预测方法

刘卓 汤健 柴天佑 余文

刘卓,  汤健,  柴天佑,  余文.  基于多模态特征子集选择性集成建模的磨机负荷参数预测方法.  自动化学报,  2021,  47(8): 1921−1931 doi: 10.16383/j.aas.c190735
引用本文: 刘卓,  汤健,  柴天佑,  余文.  基于多模态特征子集选择性集成建模的磨机负荷参数预测方法.  自动化学报,  2021,  47(8): 1921−1931 doi: 10.16383/j.aas.c190735
Liu Zhuo,  Tang Jian,  Chai Tian-You,  Yu Wen.  Selective ensemble modeling approach for mill load parameter forecasting based on multi-modal feature sub-sets.  Acta Automatica Sinica,  2021,  47(8): 1921−1931 doi: 10.16383/j.aas.c190735
Citation: Liu Zhuo,  Tang Jian,  Chai Tian-You,  Yu Wen.  Selective ensemble modeling approach for mill load parameter forecasting based on multi-modal feature sub-sets.  Acta Automatica Sinica,  2021,  47(8): 1921−1931 doi: 10.16383/j.aas.c190735

基于多模态特征子集选择性集成建模的磨机负荷参数预测方法

doi: 10.16383/j.aas.c190735
基金项目: 国家自然科学基金(61703089, 61803191, 61673097), 中央高校基本科研业务费专项资金项目(N17080400), 矿冶过程自动控制技术国家重点实验室, 矿冶过程自动控制技术北京市重点实验室(BGRIMM-KZSKL-2020-02)资助
详细信息
    作者简介:

    刘卓:东北大学流程工业综合自动化国家重点实验室讲师. 主要研究方向为复杂工业过程建模. E-mail: liuzhuo@ise.neu.edu.cn

    汤健:北京工业大学教授. 主要研究方向为小样本数据建模, 城市固废自动化处理. 本文通信作者. E-mail: freeflytang@bjut.edu.cn

    柴天佑:中国工程院院士, 东北大学教授. IEEE Fellow, IFAC Fellow, 欧亚科学院院士. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化理论、方法与技术. E-mail: tychai@mail.neu.edu.cn

    余文:墨西哥国立理工大学高级研究中心自动化部教授. 主要研究方向为复杂工业过程建模与控制, 机器学习. E-mail: yuw@ctrl.cinvestav.mx

Selective Ensemble Modeling Approach for Mill Load Parameter Forecasting Based on Multi-modal Feature Sub-sets

Funds: Supported by National Natural Science Foundation of China (61703089, 61803191, 61673097), the Fundamental Research Funds for the Central Universities (N17080400), National & Beijing Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2020-02)
More Information
    Author Bio:

    LIU Zhuo Lecturer at the State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University. Her research interest covers modeling for complex industries

    TANG Jian Professor at Beijing University of Technology. His research interest covers small sample data modeling and intelligent automatic processing of municipalsolid. Corresponding author of this paper

    CHAI Tian-You Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Fellow, IFAC Fellow, and Academician of the International Eurasian Academy of Sciences. His research interest covers adaptive control, intelligent decoupling control, and integrated automation thoery, method and technology of industrial process

    YU Wen Professor in the Departamento de Control Automatico of the Centro de Investigation de Estudios Avanzados, National Polytechnic Institute Mexico. His research interest covers modeling and control of the complex industrial process, and machine learning

  • 摘要:

    如何融合球磨机系统研磨过程所产生的多模态机械信号构建磨机负荷参数预测(Mill load parameter forecasting, MLPF)模型是当前研究的热点. 针对上述问题, 本文提出一种基于多模态特征子集选择性集成(Selective ensemble, SEN)建模的MLPF方法. 首先, 对多模态机械信号进行时频域变换得到高维频谱数据; 接着, 采用相关系数法和互信息法对多模态频谱进行线性和非线性特征子集的自适应选择; 最后, 采用优化和加权算法对上述特征子集的候选子模型进行自适应地选择与合并, 得到基于SEN机制的MLPF模型. 采用磨矿过程实验球磨机的机械信号仿真验证了所提方法的有效性.

  • 图  1  磨机系统不同位置机械信号的产生机理示意图

    Fig.  1  Generation mechanism of mechanical signals in different position of mill system

    图  2  建模策略

    Fig.  2  The proposed modeling strategy

    图  3  实验球磨机传感器布置示意图

    Fig.  3  Layout of sensors for experimental ball mill

    图  4  模态Ch1, Ch6, Ch8的频谱变量与MBVR间的相关系数和互信息值

    Fig.  4  Correlation coefficient and mutual information value between spectrum variable of mode Ch1, Ch6, Ch8 and MBVR

    表  1  面向PD的不同模态频谱特征的特征选择系数统计表

    Table  1  Coefficients statistical table of different modal spectrum feature for PD

    类别Ch1Ch2Ch3Ch4Ch5Ch6Ch7Ch8
    线性特征选择系数Min0.090500.0078680.36780.0050180.00019940.0095960.0020750.8741
    线性特征选择系数Max1.28971.73511.19131.39045.28831.26492.05641.0571
    非线性特征选择系数Min0.66440.56590.88130.84030.57180.70390.48600.9228
    非线性特征选择系数Max1.07151.08851.16801.13041.35561.13521.6231.0599
    下载: 导出CSV

    表  2  候选子模型编码

    Table  2  Coding of candidate sub-models

    序号子模型特点子模型名称子模型编码多模态通道编号
    1lin_linCorr-PLS1-81-Ch1, 2-Ch2, 3-Ch3, 4-Ch4, 5-Ch5, 6-Ch6, 7-Ch7, 8-Ch8
    2nonlin_linMi-PLS9-169-Ch1, 10-Ch2, 11-Ch3, 12-Ch4, 13-Ch5, 14-Ch6, 15-Ch7, 16-Ch8
    3lin_nonlinCorr-RWNN17-2417-Ch1, 18-Ch2, 19-Ch3, 20-Ch4, 21-Ch5, 22-Ch6, 23-Ch7, 24-Ch8
    4nonlin_nonlinMi-RWNN25-3225-Ch1, 26-Ch2, 27-Ch3, 28-Ch4, 29-Ch5, 30-Ch6, 31-Ch7, 32-Ch8
    下载: 导出CSV

    表  3  不同特征选择系数时所构建的SEN模型的预测误差和所选择的子模型编号

    Table  3  Prediction error of SEN model with different feature selection coefficients and selected sub-model number

    序号MBVRPDCVR
    测试误差集成子模型编号测试误差集成子模型编号测试误差集成子模型编号
    10.05330{ 21 23 27 31 17 32 19 24 30}0.01579{26 18 30}0.01083{14 19 26 18 30 22}
    20.06204{14 31 32 24 27 30}0.01805{25 10 31 32 14 19 24 18 30}0.009697{27 26 22 30}
    30.04515{9 17 26 14 30 27 22 32 19 24}0.01855{24 14 18 30 26}0.01146{27 14 19 26 31 18 30 22}
    40.04717{23 17 27 19 32 24 30}0.01582{14 24 26 27 32 30}0.009544{19 30 22}
    50.05231{27 17 30 23 19 32 24}0.01843{24 14 25 22 18 19 30}0.01093{20 14 31 27 32 19 26 22 30}
    60.04433{31 22 30 32 19 24}0.01452{22 14 24 32 26 19 30}0.009930{23 25 20 18 32 27 26 19 30 22}
    70.05697{31 32 24}0.01627{26 22 18 24 32 19 30}0.009870{6 20 28 19 32 18 26 27 22 30}
    80.04459{27 26 23 22 31 25 30 17 32 24}0.01687{27 18 32 19 30}0.009280{28 18 26 27 19 22 30}
    90.04969{26 32 27 30 25 19 24}0.01718{2 18 27 6 26 32 25 30}0.009650{18 32 26 25 27 19 30 22}
    100.04624{22 17 26 27 30 25 32 19 31 24}0.01748{25 26 22 32 27 6 18 19 30}0.01212{22 30}
    110.04404{25 17 18 27 22 19 30 24}0.01769{17 23 22 26 27 6 30 19 18}
    下载: 导出CSV

    表  4  磨机负荷参数各通道与多模态特征子集选择性集成模型的测试误差比较

    Table  4  Comparison of test errors between various channels of mill load parameters and multi-modal feature subset SEN model

    RMSREs备注
    MBVRPDCVR
    Corr-PLSMi-PLSCorr-RWNNMi-RWNNCorr-PLSMi-PLSCorr-RWNNMi-RWNNCorr-PLSMi-PLSCorr-RWNNMi-RWNN
    Ch10.19240.34260.13140.15030.067100.054110.069100.051610.059110.066220.070300.04930筒体振动
    Ch20.32130.72070.31030.14010.042210.044300.033210.037510.056500.047110.037110.02620筒体振动
    Ch30.44010.44310.091120.090200.120120.076110.031110.052100.11320.078310.029220.03810筒体振声
    Ch40.51250.42250.28220.20010.11420.086200.064600.11840.074420.069100.041100.04772筒体振声
    Ch50.46110.34090.19110.22210.10870.081220.11610.098100.097110.096100.044400.09911轴承振动
    Ch60.31050.21410.14310.13410.044100.037200.035200.024310.035200.036410.016300.01720轴承振动
    Ch70.38020.25020.13210.11010.10830.062410.061210.056110.094510.048110.049100.04141轴承振动
    Ch80.59340.60310.080900.36310.09710.079100.033100.032200.14210.089300.068400.03730研磨振声
    本文方法0.044040.014520.00928
    下载: 导出CSV

    表  5  磨机负荷参数各通道与多模态特征子集选择性集成模型的平均测试误差比较

    Table  5  Average test errors comparison of the various channels of mill load parameters and multi-modal feature subset SEN model

    通道MBVRPDCVR平均预测误差备注
    Ch10.13140.051610.049300.07740筒体振动
    Ch20.14010.033210.026200.06650筒体振动
    Ch30.090200.031110.029220.05020筒体振声
    Ch40.20010.064600.041100.1019筒体振声
    Ch50.19110.081220.044400.1056轴承振动
    Ch60.13410.024310.016300.05820轴承振动
    Ch70.11010.056110.041410.06920轴承振动
    Ch80.080900.032200.037300.05010研磨振声
    本文方法0.044040.014520.009280.02260
    下载: 导出CSV
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  • 收稿日期:  2019-10-23
  • 录用日期:  2020-02-07
  • 网络出版日期:  2021-08-25
  • 刊出日期:  2021-08-20

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