南京航空航天大学学报（英文版）2020，Vol.37Issue(4)：597-606,10.
一种基于离群数据检测和线性回归的压装质量智能预警方法
An Intelligent Early Warning Method of Press?Assembly Quality Based on Outlier Data Detection and Linear Regression
摘要
Abstract
Focusing on controlling the press?assembly quality of high?precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed. Linear regression is used to deal with the relationship between assembly quality and press?assembly process, then the mathematical model of displacement?force in press?assembly process is established and a qualified press?assembly force range is defined for assembly quality control. To preprocess the raw dataset of displacement?force in the press?assembly process,an improved local outlier factor based on area density and P weight( LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model. A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight. Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm, and the detection accuracy is improved by about 2％ compared with the local outlier factor based on area density (LAOF) algorithm. The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press?assembly quality of high precision servo mechanism.关键词
质量预警/离群数据检测/线性回归/基于区域密度和P权值的局部离群因子/信息熵/P权值Key words
quality early warning/outlier data detection/linear regression/local outlier factor based on area density and P weight(LAOPW)/information entropy/P weight分类
信息技术与安全科学引用本文复制引用
薛善良,李晨..一种基于离群数据检测和线性回归的压装质量智能预警方法[J].南京航空航天大学学报（英文版）,2020,37(4):597-606,10.基金项目
The authors would like to acknow-ledge the following people for their assistance:WANG Qi-quan and CHEN Guangxin from Nanjing Chenguang Group, and XIAO Xue,XU Meijiao,WU Liuyan,PENG Zhen-feng and LIU Xingxiong from College of Computer Science and Technology,Nanjing University of Aeronautics and As-tronautics. （）
