东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (8): 1089-1092.DOI: 10.12068/j.issn.1005-3026.2015.08.006

• 信息与控制 • 上一篇    下一篇

一种基于聚类分析的3MAD-MMMD过失误差侦破方法

肖冬， 包晶晶   

  1. (东北大学 信息科学与工程学院， 辽宁 沈阳110819)
  • 收稿日期:2014-07-08 修回日期:2014-07-08 出版日期:2015-08-15 发布日期:2015-08-28
  • 通讯作者: 肖冬
  • 作者简介:肖冬(1978-)，男，湖南涟源人，东北大学副教授，博士.
  • 基金资助:
    国家自然科学基金资助项目(61203214)；辽宁省教育厅科学研究一般项目(L2013101).

Detection of Gross Error Using 3MAD-MMMD Based on Cluster Analysis

XIAO Dong， BAO Jing-jing   

  1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China.
  • Received:2014-07-08 Revised:2014-07-08 Online:2015-08-15 Published:2015-08-28
  • Contact: XIAO Dong
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摘要： 软测量建模时所使用的数据集中若含有过失误差，将在很大程度上影响所建模型的精确度.因此，在建模之前，针对建模所使用的数据集，提出了基于聚类分析的集成3MAD-MMMD过失误差的侦破方法.在采集无缝钢管穿孔过程中不同变量不同时刻的数据后，将其排列成数据矩阵.首先运用3MAD算法剔除其中的单变量大误差得到新的数据矩阵，之后采用欧氏距离公式求得新矩阵中同一变量的数据到其最近点的距离，最后以所有变量最近距离的中位值d○med为检测标准，对新的数据矩阵进行过失误差侦破处理.实验和仿真图表明，3MAD-MMMD侦破方法有效地剔除了采集数据中的过失误差.

关键词: 软测量建模, 过失误差, 聚类分析, 3MAD, MMMD

Abstract: If there exist gross errors in the soft sensor modeling data， the accuracy of the model is largely affected. Therefore， for the data set to be used in the modeling process， a method of gross error detection of 3MAD-MMMD based on cluster analysis is proposed to process the data before modeling. The data of different variables in different time from the seamless pipe perforation process is collected. Then these data are arranged into a matrix. The 3MAD algorithm is used first to eliminate the large error of single-variables to get the new data matrix. Based on the Euclidean distance formula， the distance is then obtained from the data in matrix to another which is closest to it of the same variable. Finally， d○med， the median value of all variables’ closest distance， is treated as testing standards to detect gross error of new data matrix. It can be seen from the experimental and simulation results that the gross errors in the collected data sets are effectively eliminated in the 3MAD-MMMD detection method.

Key words: soft sensor modeling；gross error, cluster analysis, 3 median absolute deviation, modified median minimum distance

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