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基于流形正则化域适应随机权神经网络的湿式球磨机负荷参数软测量

贺敏 汤健 郭旭琦 阎高伟

贺敏, 汤健, 郭旭琦, 阎高伟. 基于流形正则化域适应随机权神经网络的湿式球磨机负荷参数软测量. 自动化学报, 2019, 45(2): 398-406. doi: 10.16383/j.aas.2018.c170289
引用本文: 贺敏, 汤健, 郭旭琦, 阎高伟. 基于流形正则化域适应随机权神经网络的湿式球磨机负荷参数软测量. 自动化学报, 2019, 45(2): 398-406. doi: 10.16383/j.aas.2018.c170289
HE Min, TANG Jian, GUO Xu-Qi, YAN Gao-Wei. Soft Sensor for Ball Mill Load Using DAMRRWNN Model. ACTA AUTOMATICA SINICA, 2019, 45(2): 398-406. doi: 10.16383/j.aas.2018.c170289
Citation: HE Min, TANG Jian, GUO Xu-Qi, YAN Gao-Wei. Soft Sensor for Ball Mill Load Using DAMRRWNN Model. ACTA AUTOMATICA SINICA, 2019, 45(2): 398-406. doi: 10.16383/j.aas.2018.c170289

基于流形正则化域适应随机权神经网络的湿式球磨机负荷参数软测量

doi: 10.16383/j.aas.2018.c170289
基金项目: 

国家自然科学基金 61573364

山西省煤基重点科技攻关项目 MD 2014-07

国家自然科学基金 61450011

山西省自然科学基金 2015011052

详细信息
    作者简介:

    贺敏  太原理工大学硕士研究生.主要研究方向为工业大数据应用, 机器学习, 数据驱动软测量建模与迁移学习.E-mail:hemin0215@link.tyut.edu.cn

    汤健  北京工业大学信息部教授, 博士, 主要研究方向为数据驱动软测量, 工业自动化控制.E-mail:tjian001@126.com

    郭旭琦  太原理工大学硕士研究生, 主要研究方向为智能信息处理, 数据驱动软测量建模, 迁移学习.E-mail:guoxuqi0330@link.tyut.edu.cn

    通讯作者:

    阎高伟  太原理工大学信息工程学院教授, 博士.主要研究方向为复杂工业控制系统, 智能控制理论及其应用, 智能信息处理等.本文通信作者.E-mail:yangaowei@tyut.edu.cn

Soft Sensor for Ball Mill Load Using DAMRRWNN Model

Funds: 

National Natural Science Foundation of China 61573364

Key Project of Coal Based Science and Technology in Shanxi Province MD 2014-07

National Natural Science Foundation of China 61450011

Natural Science Foundation of Shanxi Province of China 2015011052

More Information
    Author Bio:

     Master student at Taiyuan University of Technology. His research interest covers industrial large data applications, machine learning, data-driven soft sensor modeling, and transfer learning

     Ph. D., professor in the Information Department of Beijing University of Technology. His research interest covers data-driven soft sensor modeling and industrial automatic control

     Master student at Taiyuan University of Technology. Her research interest covers intelligent information processing, data-driven soft sensor modeling, and transfer learning

    Corresponding author: YAN Gao-Wei  Ph. D., professor at the College of Information Engineering of Taiyuan University of Technology. His research interest covers complex industrial control system, intelligent control theory and application, and intelligent information processing. Corresponding author of this paper
  • 摘要: 针对湿式球磨机多工况运行过程中标签样本难以获取和工况改变导致的原测量模型失准问题,本文引入域适应随机权神经网络(Domain adaptive random weight neural network,DARWNN),实现待测工况中少量标签样本与原工况样本共同进行迁移学习.DARWNN网络解决了不同工况间难以共同进行机器学习的问题,但其只考虑经验风险,而未考虑结构风险,从而泛化性能较差,预测精度较低.在此基础上,本文引入流形正则化,并构建基于流形正则化的域适应随机权神经网络(Domain adaptive manifold regularization random weight neural network,DAMRRWNN),以保持数据几何结构,提高相应模型性能.实验结果表明,所提方法可以有效提高DARWNN的学习精度,解决多工况情况下湿式球磨机负荷参数软测量问题.
    1)  本文责任编委 贺威
  • 图  1  M个目标域DARWNN算法结构

    Fig.  1  M target domain DARWNN algorithm structure

    图  2  基于流形正则化域适应随机权神经网络的软测量策略

    Fig.  2  Soft sensor strategy based on DAMRRWNN

    图  3  不同设备的数据分布差异图

    Fig.  3  Data distribution diagram for difierent devices

    图  4  光谱数据集实验结果对比

    Fig.  4  Comparison of experimental results for spectral data sets

    图  5  数据预处理流程

    Fig.  5  Data preprocessing process

    图  6  目标域为工况2时RWNN和DAMRRWNN的负荷参数预测结果

    Fig.  6  Prediction results of RWNN and DAMRRWNN on load parameter, when the condition 2 is the target domain

    图  7  目标域为工况2时三种对比算法对负荷参数浓度的预测结果

    Fig.  7  Prediction results of three algorithms on load parameter PD, when the condition 2 is the target domain

    图  8  DAMRRWNN负荷参数预测结果

    Fig.  8  DAMRRWNN load parameter prediction results

    图  9  目标域为工况2时建模方法对比

    Fig.  9  The target domain is the working condition 2 and the prediction results are compared

    图  10  三种负荷参数预测误差对比

    Fig.  10  Comparison of prediction errors of three load parameters

    表  1  m5油脂含量预测结果

    Table  1  Prediction result of oil content in m5

    评价标准 RWNN Bagging JITL
    RMSE 0.0490 0.1615 0.0803
    NRMSE 0.0655 0.2171 0.1079
    下载: 导出CSV

    表  2  m5为源域的不同算法实验结果对比

    Table  2  Comparison of experimental results of difierent algorithms for m5 as source domain

    方法 为目标域 为目标域
    油脂含量 淀粉含量 油脂含量 淀粉含量
    RMSE NRMSE RMSE NRMSE RMSE NRMSE RMSE NRMSE
    RWNN 0.7723 1.0380 1.1142 0.3056 0.8612 0.1575 1.1701 0.3209
    Bagging 0.2346 0.3153 0.9127 0.2503 0.2253 0.3028 0.9707 0.2662
    JITL 0.5866 0.7844 2.7080 0.7427 0.8021 1.0781 3.3141 0.9090
    DARWNN 0.1047 0.1407 0.5066 0.1389 0.1090 0.1465 0.5294 0.1452
    DAMRRWNN 0.0908 0.1220 0.4660 0.1278 0.0911 0.1224 0.4958 0.1360
    下载: 导出CSV

    表  3  不同工况振动信号采集次数

    Table  3  Acquisition times of vibration signals under difierent working conditions

    MFR 0.3 0.35 0.4 0.45 0.5
    次数 139 103 88 95 102
    下载: 导出CSV

    表  4  磨机负荷参数预测结果对比(RMSE)

    Table  4  Comparison of prediction results of mill load parameters (RMSE)

    建模算法 负荷参数 1-1 1-2 1-3 1-4 1-5
    RWNN MBVR 0.0279 0.6219 1.0044 1.2182 1.1312
    PD 0.0040 0.1546 0.0656 0.1763 0.2541
    CVR 0.0021 0.0817 0.3063 0.2516 0.3607
    Bagging MBVR 0.0923 0.1618 0.3379 0.3919 0.2346
    PD 0.0157 0.0470 0.0897 0.0880 0.1263
    CVR 0.0086 0.0808 0.1270 0.1395 0.2012
    JITL MBVR 0.0829 0.3688 0.4490 0.7043 0.9147
    PD 0.0138 0.0695 0.1149 0.3406 0.3467
    CVR 0.0094 0.0908 0.1428 0.1777 0.2471
    DARWNN MBVR $-$ 0.1349 0.0854 0.0843 0.0709
    PD $-$ 0.0270 0.0170 0.0153 0.0178
    CVR $-$ 0.0159 0.0114 0.0084 0.0081
    DAMRRWNN MBVR $-$ 0.1058 0.0693 0.0457 0.0365
    PD $-$ 0.0219 0.0149 0.0120 0.0162
    CVR $-$ 0.0141 0.0108 0.0074 0.0072
    下载: 导出CSV
  • [1] 汤健, 赵立杰, 柴天佑, 岳恒.基于振动频谱的磨机负荷在线软测量建模.信息与控制, 2012, 41(1):123-128 http://d.old.wanfangdata.com.cn/Periodical/xxykz201201020

    Tang Jian, Zhao Li-Jie, Chai Tian-You, Yue Heng. On-line soft-sensing modelling of mill load based on vibration spectrum. Information and Control, 2012, 41(1):123-128 http://d.old.wanfangdata.com.cn/Periodical/xxykz201201020
    [2] 汤健, 田福庆, 贾美英, 李东.基于频谱数据驱动的旋转机械设备负荷软测量.北京:国防工业出版社, 2015.

    Tang Jian, Tian Fu-Qing, Jia Mei-Ying, Li Dong. Soft Sensing of Rotating Machinery Equipment Load Based on Spectrum Data Drive. Beijing:National Defense Industry Press, 2015.
    [3] 汤健, 赵立杰, 岳恒, 柴天佑.湿式球磨机筒体振动信号分析及负荷软测量.东北大学学报(自然科学版), 2010, 31(11):1521-1524 http://d.old.wanfangdata.com.cn/Periodical/dbdxxb201011001

    Tang Jian, Zhao Li-Jie, Yue Heng, Chai Tian-You. Analysis of vibration signal of wet ball mill shell and soft sensoring for mill load. Journal of Northeastern University (Natural Science), 2010, 31(11):1521-1524 http://d.old.wanfangdata.com.cn/Periodical/dbdxxb201011001
    [4] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine:theory and applications. Neurocomputing, 2006, 70(1-3):489-501 doi: 10.1016/j.neucom.2005.12.126
    [5] Tang J, Deng C, Huang G B. Extreme learning machine for multilayer perceptron. IEEE Transactions on Neural Networks and Learning Systems, 2017, 27(4):809-821 http://ieeexplore.ieee.org/document/7103337
    [6] Maass W. Liquid state machines: motivation, theory, and applications. Computability in Context: Computation and Logic in the Real World. Hackensack, NJ: Imperial College Press, 2009. 275-296
    [7] Zhang M, Liu X G, Zhang Z Y. A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine. Chinese Journal of Chemical Engineering, 2016, 24(8):1013-1019 doi: 10.1016/j.cjche.2016.05.030
    [8] 汤健, 柴天佑, 余文, 赵立杰.在线KPLS建模方法及在磨机负荷参数集成建模中的应用.自动化学报, 2013, 39(5):471-486 http://www.aas.net.cn/CN/abstract/abstract17934.shtml

    Tang Jian, Chai Tian-You, Yu Wen, Zhao Li-Jie. On-line KPLS algorithm with application to ensemble modeling parameters of mill load. Acta Automatica Sinica, 2013, 39(5):471-486 http://www.aas.net.cn/CN/abstract/abstract17934.shtml
    [9] Shao W M, Tian X M, Wang P. Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor. Chinese Journal of Chemical Engineering, 2015, 23(12):1925-1934 doi: 10.1016/j.cjche.2015.11.012
    [10] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359 doi: 10.1109/TKDE.2009.191
    [11] Zhang L, Zhang D. Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Transactions on Instrumentation and Measurement, 2015, 64(7):1790-1801 doi: 10.1109/TIM.2014.2367775
    [12] Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(5000):2319-2323 doi: 10.1126-science.290.5500.2319/
    [13] Liu B, Xia S X, Meng F R, Zhou Y. Manifold regularized extreme learning machine. Neural Computing and Applications, 2016, 27(2):255-269 http://d.old.wanfangdata.com.cn/Periodical/txxb201611007
    [14] Schmidt W F, Kraaijveld M A, Duin R P W. Feedforward neural networks with random weights. In: Proceedings of the 11th IAPR International Conference on Pattern Recognition Vol.Ⅱ Conference B: Pattern Recognition Methodology & Systems. The Hague, Netherlands: IEEE, 1992. 1-4
    [15] Liu X, Lin S B, Fang J, Xu, Z B. Is extreme learning machine feasible? A theoretical assessment (Part Ⅰ). IEEE Transactions on Neural Networks and Learning Systems, 2014, 26(1):7-20 http://ieeexplore.ieee.org/document/6862852/
    [16] 韩敏, 李德才.基于替代函数及贝叶斯框架的1范数ELM算法.自动化学报, 2011, 37(11):1344-1350 http://www.aas.net.cn/CN/abstract/abstract17624.shtml

    Han Min, Li De-Cai. A norm 1 regularization term ELM algorithm based on surrogate function and Bayesian framework. Acta Automatica Sinica, 2011, 37(11):1344-1350 http://www.aas.net.cn/CN/abstract/abstract17624.shtml
    [17] Deng W Y, Zheng Q H, Chen L. Regularized extreme learning machine. In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009. CIDM'09. Nashville, TN, USA: IEEE, 2009. 389-395
    [18] Tomar V S, Rose R C. Manifold regularized deep neural networks. In: Proceedings of the 15th Annual Conference of the International Speech Communication Association. Singapore: ISCA, 2014. 348-352
    [19] Guan N Y, Tao D C, Luo Z G, Yuan B. Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent. IEEE Transactions on Image Processing, 2011, 20(7):2030-2048 doi: 10.1109/TIP.2011.2105496
    [20] 徐嘉明, 张卫强, 杨登舟, 刘加, 夏善红.基于流形正则化极限学习机的语种识别系统.自动化学报, 2015, 41 (9):1680-1685 http://www.aas.net.cn/CN/abstract/abstract18741.shtml

    Xu Jia-Ming, Zhang Wei-Qiang, Yang Deng-Zhou, Liu Jia, Xia Shan-Hong. Manifold regularized extreme learning machine for language recognition. Acta Automatica Sinica, 2015, 41(9):1680-1685 http://www.aas.net.cn/CN/abstract/abstract18741.shtml
    [21] Amar M, Gondal I, Wilson C. Vibration spectrum imaging:a novel bearing fault classification approach. IEEE Transactions on Industrial Electronics, 2015, 62(1):494-502 doi: 10.1109/TIE.2014.2327555
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  • 收稿日期:  2017-05-26
  • 录用日期:  2017-08-29
  • 刊出日期:  2019-02-20

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