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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

DOI: 10.3724/SP.J.1004.2014.01853, PP. 1853-1866

Keywords: 经验模态分解,选择性集成建模,磨机负荷参数,选择性信息融合,频谱特征

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Abstract:

?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(Empiricalmodedecomposition,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(Intrinsicmodefunction,IMF);接着在频域内基于互信息(Mutualinformation,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(Kernelpartialleastsquare,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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