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AuthorAbdella, Galal M.
AuthorAl-Khalifa, Khalifa N.
AuthorKim, Sangahn
AuthorJeong, Myong K.
AuthorElsayed, Elsayed A.
AuthorHmouda, Abdel Magid
Available date2020-11-04T10:00:41Z
Publication Date2017
Publication NameQuality and Reliability Engineering International
ResourceScopus
URIhttp://dx.doi.org/10.1002/qre.2041
URIhttp://hdl.handle.net/10576/16904
AbstractHigh-dimensional applications pose a significant challenge to the capability of conventional statistical process control techniques in detecting abnormal changes in process parameters. These techniques fail to recognize out-of-control signals and locate the root causes of faults especially when small shifts occur in high-dimensional variables under the sparsity assumption of process mean changes. In this paper, we propose a variable selection-based multivariate cumulative sum (VS-MCUSUM) chart for enhancing sensitivity to out-of-control conditions in high-dimensional processes. While other existing charts with variable selection techniques tend to show weak performances in detecting small shifts in process parameters due to the misidentification of the 'faulty' parameters, the proposed chart performs well for small process shifts in identifying the parameters. The performance of the VS-MCUSUM chart under different combinations of design parameters is compared with the conventional MCUSUM and the VS-multivariate exponentially weighted moving average control charts. Finally, a case study is presented as a real-life example to illustrate the operational procedures of the proposed chart. Both the simulation and numerical studies show the superior performance of the proposed chart in detecting mean shift in multivariate processes.
Languageen
PublisherJohn Wiley and Sons Ltd
Subjectaverage run length (ARL)
multivariate statistical process control
VS-MCUSUM
VS-MEWMA variable selection
TitleVariable Selection-based Multivariate Cumulative Sum Control Chart
TypeArticle
Pagination565-578
Issue Number3
Volume Number33
dc.accessType Abstract Only


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