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基于特征空间k最近邻的批次过程监视

郭小萍 袁杰 李元

郭小萍, 袁杰, 李元. 基于特征空间k最近邻的批次过程监视. 自动化学报, 2014, 40(1): 135-142. doi: 10.3724/SP.J.1004.2014.00135
引用本文: 郭小萍, 袁杰, 李元. 基于特征空间k最近邻的批次过程监视. 自动化学报, 2014, 40(1): 135-142. doi: 10.3724/SP.J.1004.2014.00135
GUO Xiao-Ping, YUAN Jie, LI Yuan. Feature Space k Nearest Neighbor Based Batch Process Monitoring. ACTA AUTOMATICA SINICA, 2014, 40(1): 135-142. doi: 10.3724/SP.J.1004.2014.00135
Citation: GUO Xiao-Ping, YUAN Jie, LI Yuan. Feature Space k Nearest Neighbor Based Batch Process Monitoring. ACTA AUTOMATICA SINICA, 2014, 40(1): 135-142. doi: 10.3724/SP.J.1004.2014.00135

基于特征空间k最近邻的批次过程监视

doi: 10.3724/SP.J.1004.2014.00135
基金项目: 

国家自然科学基金(60774070,61034006,61174119)资助

详细信息
    作者简介:

    郭小萍 沈阳化工大学信息工程学院副教授. 主要研究方向为基于数据驱动技术的复杂过程故障检测与诊断.E-mail:gxp2001@sina.com

Feature Space k Nearest Neighbor Based Batch Process Monitoring

Funds: 

Supported by National Natural Science Foundation of China (60774070, 61034006, 61174119)

  • 摘要: 针对具有非高斯、非线性及多工况特性的批次过程,提出一种基于特征量最近邻统计指标的过程监视方法. 首先,将批次过程正常工况原始数据投影到其特征空间,提取主元T和平方预测误差SPE,并进行特征量k最近邻距离平方和的求解. 然后,采用核密度估计法获得概率密度分布函数,确定统计监视控制限. 特征空间的主元T和SPE特征量能全面代表原始数据的有用信息. 采用特征量k最近邻建立监视模型将会节省存储空间,提高建模样本数量与变量之比以及检测异常工况的速度. 另外,利用局部近邻数据建模可以解决过程具有的非线性和多工况问题,而应用核密度估计法可以解决过程数据具有的非高斯分布问题. 最后,在半导体生产过程的成功应用表明了所提方法的有效性.
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出版历程
  • 收稿日期:  2012-04-23
  • 修回日期:  2012-08-31
  • 刊出日期:  2014-01-20

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