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AuthorAbdella, Galal M.
AuthorMaleki, Mohammad Reza
AuthorKim, Sangahn
AuthorAl-Khalifa, Khalifa N.
AuthorHamouda, Abdel Magid S.
Available date2024-06-23T06:32:20Z
Publication Date2020
Publication NameComputers and Industrial Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.cie.2020.106465
ISSN3608352
URIhttp://hdl.handle.net/10576/56162
AbstractHigh-dimensional variability monitoring and diagnosing is of great prominence for the quality improvement and cost reduction. Most of the existing control charts are mainly based on the assumption that the in-control covariance matrix is known in prior. This paper proposes a new control chart for monitoring of variability of high-dimensional process under the sparsity conditions. The proposed control chart uses the adaptive thresholding LASSO rule for estimating the unknown covariance matrix. To evaluate the performance of the proposed chart, named as T-COV, the signal probability was estimated under several patterns of out-of-control conditions and compared with the conditional entropy (CE) control chart. This paper uses the process of spur gear production as a real-world example to illustrate the operating procedures of the T-COV chart. The results of the simulation and real studies have revealed the advantage of the T-COV chart over the CE in quickly capturing and precisely diagnosing the deviation in the covariance matrix of the high-dimensional processes.
SponsorThis paper was made possible by NPRP grant No. 7-1040-2-393 from the Qatar National Research Fund and by internal grant No. QUSD-CENG-2018\2019-5 from Qatar University, Doha-Qatar . The authors of this paper would like to thank the anonymous reviewers for their helpful comments that highly contributed to enhancing the quality of this paper.
Languageen
PublisherElsevier
SubjectAdaptive thresholding
Covariance matrix monitoring
Phase I process control
Signal probability
Sparsity property
TitlePhase-I monitoring of high-dimensional covariance matrix using an adaptive thresholding LASSO rule
TypeArticle
Volume Number144
dc.accessType Abstract Only


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