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基于EMD和选择性集成学习算法的磨机负荷参数软测量

汤健 柴天佑 丛秋梅 苑明哲 赵立杰 刘卓 余文

汤健, 柴天佑, 丛秋梅, 苑明哲, 赵立杰, 刘卓, 余文. 基于EMD和选择性集成学习算法的磨机负荷参数软测量. 自动化学报, 2014, 40(9): 1853-1866. doi: 10.3724/SP.J.1004.2014.01853
引用本文: 汤健, 柴天佑, 丛秋梅, 苑明哲, 赵立杰, 刘卓, 余文. 基于EMD和选择性集成学习算法的磨机负荷参数软测量. 自动化学报, 2014, 40(9): 1853-1866. doi: 10.3724/SP.J.1004.2014.01853
TANG Jian, CHAI Tian-You, CONG Qiu-Mei, YUAN Ming-Zhe, ZHAO Li-Jie, LIU Zhuo, YU Wen. Soft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm. ACTA AUTOMATICA SINICA, 2014, 40(9): 1853-1866. doi: 10.3724/SP.J.1004.2014.01853
Citation: TANG Jian, CHAI Tian-You, CONG Qiu-Mei, YUAN Ming-Zhe, ZHAO Li-Jie, LIU Zhuo, YU Wen. Soft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm. ACTA AUTOMATICA SINICA, 2014, 40(9): 1853-1866. doi: 10.3724/SP.J.1004.2014.01853

基于EMD和选择性集成学习算法的磨机负荷参数软测量

doi: 10.3724/SP.J.1004.2014.01853
基金项目: 

国家自然科学基金(61034008,61004051,61203102,61020106003,61134006),111计划(B08015),国家支撑计划(2012-BAF19G00),中国博士后科学基金(2013M532118,2013M530953,2013M541820)资助

详细信息
    作者简介:

    汤健 北京交通大学计算技术研究所博士后.1998年获得海军工程学院工学学士学位,2006年和2012年获得东北大学控制理论与控制工程专业硕士和博士学位.主要研究方向为工业过程综合自动化系统,基于数据驱动的软测量和复杂系统建模与仿真.

    通讯作者:

    柴天佑 东北大学教授.主要研究方向为自适应控制,智能解耦控制和流程工业综合自动化理论、方法与技术.本文通信作者.E-mail:tychai@mail.neu.edu.cn

Soft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm

Funds: 

Supported by National Natural Science Foundation of China(61034008, 61004051, 61203102, 61020106003, 61134006), the 111 Project (B08015), National Key Technology Support Program Project(2012-BAF19G00), National Science Foundation for Post-doctoral Scientists of China (2013M532118, 2013M530953, 2013M541820)

  • 摘要: 针对磨机筒体振动和振声信号组成复杂难以解释、蕴含信息存在冗余性和互补性、与磨机负 荷参数映射关系难以描述等问题,提出了基于经验模态分解(Empirical mode decomposition,EMD)技术和选择性集成学习算法分析 筒体振动与振声信号组成,建立磨机负荷参数软测量模型的新方法.首先从机理上定性分析了筒 体振动及振声信号组成的复杂性;然后采用EMD技术将原始信号自适应分解为具有不同时间尺度的系列组 成成分,即本征模态函数(Intrinsic mode function,IMF);接着在频域内基于互信息(Mutual information,MI)方法分析并选择IMF频谱特征;最后采用基 于核偏最小二乘(Kernel partial least square,KPLS)建模方法、分支定界优化算法的选择性集成学习方法建立磨机负荷参数软测量模型,实现了多源多尺度频谱特征的选择性信息融合.基于实验球磨机的实际运行数据仿真验证了该方法的有效性.
  • [1] Wei D H, Craig I K. Grinding mill circuits——a survey of control and economic concerns. International Journal of Mineral Process, 2009, 90(1-4): 56-66
    [2] Chai Tian-You. Operational optimization and feedback control for complex industrial processes. Acta Automatica Sinica, 2013, 39(11): 1744-1757(柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744-1757)
    [3] Tang Jian, Zhao Li-Jie, Yue Heng, Chai Tian-You. Present status and future developments of detection method for mill load. Control Engineering of China, 2010, 17(5): 565-570(汤健, 赵立杰, 岳恒, 柴天佑. 磨机负荷检测方法研究综述. 控制工程, 2010, 17(5): 565-570)
    [4] Zhou P, Chai T Y, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing. IEEE Transactions on Automation Science and Engineering, 2009, 6(4): 730-743
    [5] Huang P, Jia M P, Zhong B L. Investigation on measuring the fill level of an industrial ball mill based on the vibration characteristics of the mill shell. Minerals Engineering, 2009, 14(22): 1200-1208
    [6] Tang J, Zhao L J, Zhou J W, Yue H, Chai T Y. Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell. Minerals Engineering, 2010, 23(9): 720-730
    [7] Das S P, Das D P, Behera S K, Mishra B K. Interpretation of mill vibration signal via wireless sensing. Minerals Engineering, 2011, 24(3-4): 245-251
    [8] Feng Tian-Jing, Wang Huan-Gang, Xu Wen-Li, Xu Ning. An on-line mill load monitoring system based on shell vibration signals. Mining & Metallurgy, 2012, 19(2): 66-69(冯天晶, 王焕钢, 徐文立, 徐宁. 基于筒壁振动信号的磨机工况检测系统. 选矿, 2012, 19(2): 66-69
    [9] Tang J, Chai T Y, Yu W, Zhao L J. Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process. Control Engineering Practice, 2012, 20(10): 991-1004
    [10] Gui Wei-Hua, Yang Chun-Hua, Chen Xiao-Fang, Wang Ya-Lin. Modeling and optimization problems and challenges arising in nonferrous metallurgical processes. Acta Automatica Sinica, 2013, 39(3): 197-207(桂卫华, 阳春华, 陈晓方, 王雅琳. 有色冶金过程建模与优化的若干问题及挑战. 自动化学报, 2013, 39(3): 197-207)
    [11] Tang Jian, Chai Tian-You, Zhao Li-Jie, Yue Heng, Zheng Xiu-Ping. Ensemble modeling for parameters of ballmill load in grinding process based on frequency spectrum of shell vibration. Control Theory & Applications, 2012, 29(2): 183-191(汤健, 柴天佑, 赵立杰, 岳恒, 郑秀萍. 基于振动频谱的磨矿过程球磨机负荷参数集成建模方法. 控制理论与应用, 2012, 29(2): 183-191)
    [12] Tang J, Chai T Y, Yu W, Zhao L J. Modeling load parameters of ball mill in grinding process based on selective ensemble multisensor information. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 726-740
    [13] Hao Hong-Wei, Wang Zhi-Bin, Yin Xu-Cheng, Chen Zhi-Qiang. Dynamic selection and circulating combination for multiple classier systems. Acta Automatica Sinica, 2013, 39(11): 1290-1295(郝红卫, 王志彬, 殷绪成, 陈志强. 分类器的动态选择与循环集成方法. 自动化学报, 2013, 39(11): 1290-1295)
    [14] Tang Jian, Zhao Li-Jie, Chai Tian-You, Yue Heng. On-line soft-sensing modeling of mill load based on vibration spectrum. Information and Control, 2012, 41(1): 123-128(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)
    [15] 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(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)
    [16] Huang N E, Long S R, Shen Z. The mechanism for frequency downshift in nonlinear wave evolution. Advances in Applied Mechanics, 1996, 32: 59-117
    [17] Yang J N, Lei Y, Pan S W, Huang N. System identification of linear structure based on Hilbert-Huang spectral analysis. Part 1: Normal Modes. Earthquake Engineering & Structure Dynamics, 2003, 32(9): 1443-1467
    [18] Yan R Q, Gao R X. Rotary machine health diagnosis based on empirical mode decomposition. Journal of Vibration an Acoustics, 20081, 130(2): 1-12
    [19] Chen J. Application of empirical mode decomposition in structural health monitoring: some experience. Advances in Adaptive Data Analysis, 2009, 1(4): 601-621
    [20] Huang P, Pan Z W, Qi X L, Lei J P. Bearing fault diagnosis based on EMD and PSD. In: Proceedings of the 8th World Congress on Intelligent Control and Automation, WCICA 2010. Ji'nan, China: IEEE, 2010. 1300-1304
    [21] Tang J, Zhao L J, Yue H, Yu W, Chai T Y. Vibration analysis based on empirical mode decomposition and partial least square. Procedia Engineering, 2011, 16: 646-652
    [22] Zhao L J, Tang J, Zheng W R. Ensemble modeling of mill load based on empirical mode decomposition and partial least squares. Journal of Theoretical and Applied Information Technology, 2012, 45(1): 179-191
    [23] Tang J, Zhao L J, Long J, Chai T Y, Yu W. Selective ensemble modeling parameters of mill load based on shell vibration signal. Lecture Notes in Computer Science, 2012, 7367: 489-497
    [24] Tang Jian. Soft Sensoring of Ball Mill Load for Grinding Process [Ph.D. dissertation], Northeastern University, China, 2012 (汤健. 磨矿过程磨机负荷软测量方法研究 [博士学位论文], 东北大学, 中国, 2012)
    [25] Peng H C, Long F H, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238
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出版历程
  • 收稿日期:  2013-06-14
  • 修回日期:  2013-11-26
  • 刊出日期:  2014-09-20

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