一种基于聚类分析的3MAD-MMMD过失误差侦破方法

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dmed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.

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

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.