Paper Info
Title
Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown
Abstract
Memory-type multivariate charts have been widely recognized as a potentially powerful process monitoring tool because of their excellent speed in detecting small-to-moderate shifts in the mean vector of a multivariate normally distributed process, namely, the multivariate EWMA (MEWMA), double MEWMA, Crosier multivariate CUSUM (MCUSUM), and Pignatiello and Runger MCUSUM charts. These multivariate charts are based on the assumption that the covariance matrix is known in advance; but, it may not be known in practice. It is thus not possible to use these multivariate charts unless a large Phase I dataset is available from an in-control process. In this paper, we propose multivariate charts with fixed and variable sampling intervals for the process mean vector when the covariance matrix is estimated from sample. Using the Monte Carlo simulation method, the run length characteristics of the multivariate charts are computed. It is shown that the in-control and out-of-control run length performances of the proposed multivariate charts are robust to the changes in the process covariance matrix, while the existing multivariate charts are not. A real dataset is taken to explain the implementation of the proposed multivariate charts.
Year
DOI
Venue
2020
10.1002/qre.2564
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
Keywords
Field
DocType
average run length,control chart,DMEWMA,MCUSUM,Monte Carlo simulation,MEWMA,process mean,statistical process control
Multivariate statistics,Sampling (statistics),Covariance matrix,Engineering,Statistics
Journal
Volume
Issue
ISSN
36.0
1.0
0748-8017
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Abdul Haq16318.42
Michael B. C. Khoo228249.97