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AuthorTurkoz M.
AuthorKim S.
AuthorJeong Y.-S.
AuthorJeong M.K.
AuthorElsayed E.A.
AuthorAl-Khalifa K.N.
AuthorHamouda A.M.
Available date2020-04-09T07:35:00Z
Publication Date2019
Publication NameJournal of Quality Technology
ResourceScopus
ISSN224065
URIhttp://dx.doi.org/10.1080/00224065.2018.1507561
URIhttp://hdl.handle.net/10576/13917
AbstractIn most manufacturing processes, identifying the faulty process variables that may lead to process changes is crucial for quality engineers and practitioners. There are several parametric procedures for identifying faulty variables with the assumption that they follow multivariate normal distributions. However, in practice, the normality assumption restricts the applicability of such procedures in identifying the faulty variables. In addition, conventional procedures for fault identification are often computationally expensive, especially in high-dimensional processes. Therefore, this article proposes a data-driven Bayesian approach for fault identification that addresses the limitations posed by the normality assumption. The proposed approach is computationally efficient for high-dimensional data compared with existing approaches. Experimental results with various simulation studies and real-life data sets demonstrate the effectiveness of the proposed procedure. - 2018, - 2018 American Society for Quality.
SponsorThis article was made possible by the support of NPRP 5-364-2-142 and NPRP 7-1040-2-393 grants from Qatar National Research Fund (QNRF) and NRF-2015R1C1A1A01051487 from the National Research Foundation of Korea.
Languageen
PublisherTaylor and Francis Inc.
SubjectBayesian statistics
data-driven
faulty variable identification
multivariate statistical process control
support vector data description
TitleBayesian framework for fault variable identification
TypeArticle
Pagination375-391
Issue Number4
Volume Number51
dc.accessType Abstract Only


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