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COMPARISON OF MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE AND GENERALIZED VARIANCE |S| PROCEDURES WITH INDUSTRIAL APPLICATION
International Journal of Applied Mathematics • 2018
Publication Information
Authors
Mohamed Hamed
Keywords
Average Run Length Performance, Monitoring Process, Multivariate Statistical Analysis, Quality Control
Journal
International Journal of Applied Mathematics
Publisher
International Academy of Science, Engineering and Technology
Volume
7
Issue
5
Pages
19-28
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
In this paper, comparing between two procedures: Multivariate Exponentially Weighted Moving Average
(MEWMA) quality control chart and Generalized Variance || quality control chart. The first procedure MEWMA is an
example of a multivariate charting scheme whose monitoring statistic is unable to determine which variable caused the
signal. The second procedure is a Generalized Variance || quality control chart for the multivariate process, it is a very
powerful way to detect small shifts in the mean vector. Generalized variance chart allows us to simultaneously monitor
whether the joint variability of two or more related variables is in control. In addition, this paper provides a comparison
between MEWMA and generalized variance |S| multivariate control chart procedures by application with real data.
(MEWMA) quality control chart and Generalized Variance || quality control chart. The first procedure MEWMA is an
example of a multivariate charting scheme whose monitoring statistic is unable to determine which variable caused the
signal. The second procedure is a Generalized Variance || quality control chart for the multivariate process, it is a very
powerful way to detect small shifts in the mean vector. Generalized variance chart allows us to simultaneously monitor
whether the joint variability of two or more related variables is in control. In addition, this paper provides a comparison
between MEWMA and generalized variance |S| multivariate control chart procedures by application with real data.