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Hotelling T2 chart using the generalized multiple dependent state sampling scheme

Hotelling T2 chart using the generalized multiple dependent state sampling scheme This research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.Design/methodology/approachThis chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.FindingsIt was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.Originality/valueIn this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Quality & Reliability Management Emerald Publishing

Hotelling T2 chart using the generalized multiple dependent state sampling scheme

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0265-671X
DOI
10.1108/ijqrm-03-2020-0084
Publisher site
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Abstract

This research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.Design/methodology/approachThis chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.FindingsIt was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.Originality/valueIn this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

Journal

International Journal of Quality & Reliability Management – Emerald Publishing

Published: May 12, 2021

Keywords: Quality control; Hotelling T2; Multivariate control chart; Generalized multiple dependent state; Genetic algorithm

References

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