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基于振动频谱的磨机负荷在线软测量建模

ISSN号：1002-0411
期刊名称：《信息与控制》
时间：0
分类：TP29[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
作者机构：[1]东北大学流程工业综合自动化教育部重点实验室,辽宁沈阳110004, [2]沈阳化工大学信息工程学院,辽宁沈阳110142, [3]东北大学自动化研究中心,辽宁沈阳110004
相关基金：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（2008BAB31803）.

关键词：在线软测量, 递归主元分析, 最小二乘支持向量回归机, 磨机负荷, 振动频谱, on-line soft sensing, recursive principal component analysis, least square support vector regression, mill load, vibration spectrum

中文摘要：

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，基于磨机筒体振动频谱，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．

英文摘要：

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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