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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.
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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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