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AuthorAbdella G.M.
AuthorKim J.
AuthorKim S.
AuthorAl-Khalifa K.N.
AuthorJeong M.K.M.K.
AuthorHamouda A.M.
AuthorElsayed E.A.
Available date2020-04-27T08:34:21Z
Publication Date2019
Publication NameJournal of Quality Technology
ResourceScopus
ISSN224065
URIhttp://dx.doi.org/10.1080/00224065.2019.1569952
URIhttp://hdl.handle.net/10576/14594
AbstractIn high-dimensional processes, monitoring process variability is considerably difficult due to the large number of variables and the limited number of samples. Monitoring changes in the covariance matrix of a multivariate process is often used for monitoring process variability under the assumption that only a few elements in the covariance matrix are changed simultaneously from the in-control values. The existing LASSO-based covariance monitoring charts in the high-dimensional settings provide good performance in detecting some shift patterns depending on the prespecified tuning parameter. In practice, control charts that perform reasonably well over various shift patterns are desired when shift patterns are unknown. In this article, we propose a control chart based on an adaptive LASSO-thresholding for monitoring changes in the covariance matrix. The performance of the proposed chart, which is called the ALT-norm chart, is evaluated for various shift patterns and compared with the existing penalized likelihood-based methods. The results show the effectiveness of the proposed chart. Finally, we illustrate the advantages of the ALT-norm chart through simulated and real data from both the semiconductor industry and a high-dimensional milling process. - 2019 American Society for Quality.
SponsorThis publication was made possible by the NPRP award [NPRP 05-563-2-142] and [NPRP-7 - 1040 - 2 - 393] from the Qatar National Research Fund (a member of The Qatar Foundation).
Languageen
PublisherTaylor and Francis Inc.
Subjectadaptive thresholding estimation
monitoring covariance matrix
multivariate statistical process control
TitleAn adaptive thresholding-based process variability monitoring
TypeArticle
Pagination242-256
Issue Number3
Volume Number51
dc.accessType Abstract Only


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