Monitoring multivariate coefficient of variation for high‐dimensional processes

Multivariate coefficient of variation (MCV) charts are effective tools for monitoring process relative variability. They are developed on the assumption that the process subgroup size available for monitoring the MCV parameter is larger than the number of process characteristics. In such a case, the...

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Bibliographic Details
Published inQuality and reliability engineering international Vol. 38; no. 5; pp. 2606 - 2621
Main Authors Adegoke, Nurudeen A., Dawod, Abdaljbbar, Adeoti, Olatunde Adebayo, Sanusi, Ridwan A., Abbasi, Saddam Akber
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.07.2022
Subjects
Carbon fibers
Coefficient of variation
Control charts
Covariance matrix
high‐dimensional
LASSO
likelihood ratio test
Mathematical analysis
Monitoring
Multivariate analysis
process monitoring
Process parameters
relative variability
shrinkage covariance matrix
Subgroups
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Summary:Multivariate coefficient of variation (MCV) charts are effective tools for monitoring process relative variability. They are developed on the assumption that the process subgroup size available for monitoring the MCV parameter is larger than the number of process characteristics. In such a case, the unbiased estimates of the in‐control mean vector and covariance matrix are used to calculate the chart monitoring statistic. Here, we study the performance of MCV control charts when only a small subgroup size is available for estimating the in‐control mean vector and covariance matrix. We examine the use of a shrinkage estimate of the covariance matrix and propose two one‐sided upward and downward least absolute shrinkage and selection operator (LASSO)‐based MCV charts for detecting upward and downward shifts in the process MCV parameter, respectively. Our simulation study shows that the LASSO‐based MCV charts outperform the classical two one‐sided MCV charts when small subgroup sizes are available for monitoring. The improved performance of the proposed LASSO‐based MCV charts in monitoring shifts in the MCV parameter is demonstrated via an illustrative case study of carbon fiber tube application, where changes are detected earlier than the classical two one‐sided MCV charts.
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3094