Title :
Identifying the mean vector of bivariate process
Author :
Yongman, Zhao ; Zhen, He ; Shugaung, He
Author_Institution :
Dept. of Ind. Eng., Tianjin Univ., Tianjin, China
Abstract :
Control charting is the most important part of implementing SPC. However, this usually makes some incorrect control limits in multivariate setting when the variables are highly correlated. This study proposes the joint X charts identifying the directions of out-of-control variables about bivariate when the joint non-central chi-square statistic (NCS) charts monitor the mean shift in bivariate control chart. Firstly the joint NCS charts can detect the variable or group of variables that cause the signal and then the joint X charts recognize the direction(s). Once the joint NCS charts and the proposed charts signal simultaneously, the user can be provided more details of the out-of-control variable. That makes technician and/or engineering analyze and correct them more conveniently and speed up the research about monitoring and diagnosing of multivariate process.
Keywords :
control charts; monitoring; statistical process control; bivariate control chart; bivariate process; control charting; joint X charts; joint noncentral chi-square statistic charts; mean shift; mean vector identification; multivariate process diagnosing; multivariate process monitoring; statistical process control; Control charts; Covariance matrix; Joints; Manufacturing; Monitoring; Process control; Quality control; bivariate process; control chart; joint NCS charts; joint X charts; mean vector;
Conference_Titel :
Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2011 IEEE International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-61284-910-2
DOI :
10.1109/CYBER.2011.6011782