基于振动频谱的磨机负荷在线软测量建模

汤健, 赵立杰, 柴天佑, 岳恒

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[J]. 信息与控制, 2012, 41(1): 123-128.
引用本文: 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[J]. 信息与控制, 2012, 41(1): 123-128.
TANG Jian, ZHAO Lijie, CHAI Tianyou, YUE Heng. On-line Soft-sensing Modelling of Mill Load Based on Vibration Spectrum[J]. INFORMATION AND CONTROL, 2012, 41(1): 123-128.
Citation: TANG Jian, ZHAO Lijie, CHAI Tianyou, YUE Heng. On-line Soft-sensing Modelling of Mill Load Based on Vibration Spectrum[J]. INFORMATION AND CONTROL, 2012, 41(1): 123-128.

基于振动频谱的磨机负荷在线软测量建模

基金项目: 国家自然科学基金资助项目(61020106003,60874057);中国博士后科学基金资助项目(20100471464);国家科技支撑计划资助项目(2008BAB31B03)
详细信息
    作者简介:

    汤健(1974- ),男,博士生,工程师.研究领域为综合自动化系统,基于数据的软测量建模等.
    赵立杰(1972- ),女,博士,副教授.研究领域为多元统计分析方法,工业过程性能监视和故障诊断.
    柴天佑(1947- ),男,博士,教授.研究领域为自适应控制,多变量智能解耦,工业过程综合自动化等.

    通讯作者:

    汤健, tjian001@126.com

  • 中图分类号: TP29

On-line Soft-sensing Modelling of Mill Load Based on Vibration Spectrum

  • 摘要: 针对磨机负荷(ML)软测量模型难以适应磨矿过程的时变特性,模型需要依据工况实时在线更新的问题,基于磨机简体振动频谱,通过递归主元分析(RPCA)和在线最小二乘支持向量回归机(LSSVR)的集成,提出了ML参数(料球比、矿浆浓度、充填率)在线软测量方法.首先,针对训练样本,采用主元分析(PCA)分别提取振动频谱在低、中、高频段的谱主元;然后以串行组合后的谱主元为输入,采用LSSVR方法构造ML参数离线软测量模型;最后,采用旧模型完成预测后,应用RPCA及在线LSSVR算法分别递归更新模型的输入和模型的回归参数,从而实现了ML软测量模型的在线更新.实验结果表明,该软测量方法与其它常规方法相比具有较高的精度和更好的预测性能.
    Abstract: The soft-sensing model for mill load(ML) is difficult to adapt to the time-varying characters of the mineral process,and it needs to be updated online in real-time according to the changes of condition.Aiming at these problems, based on the vibration spectrum of the mill shell,an on-line soft-sensing approach is proposed to measure the ML parameters, such as material to ball volume ratio(MBVR),pulp density(PD) and charge volume ratio(CVR) inside the mill.The method is realized by the integration of recursive principal component analysis(RPCA) and on-line least square support vector regression(LSSVR).At first,for the training samples,spectral principal components(PCs) at low,medium and high frequency bands of the shell vibration spectrum are extracted through PCA.Then,the spectral PCs of serial combination with different bands are used to construct ML parameters off-line soft sensing models based on LSSVR.At last,when a new sample is given,after predicted with the older models,the inputs and regression parameters of the soft sensing models are updated by RPCA and on-line LSSVR algorithm respectively.Therefore,the on-line updating of the soft-sensing models for ML parameters are implemented.Experiment result shows that the proposed approach has higher accuracy and better predictive performance than other normal approaches.
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出版历程
  • 收稿日期:  2010-10-20
  • 发布日期:  2012-02-19

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