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المؤلفTurkoz M.
المؤلفKim S.
المؤلفJeong Y.-S.
المؤلفJeong M.K.
المؤلفElsayed E.A.
المؤلفAl-Khalifa K.N.
المؤلفHamouda A.M.
تاريخ الإتاحة2020-04-09T07:35:00Z
تاريخ النشر2019
اسم المنشورJournal of Quality Technology
المصدرScopus
الرقم المعياري الدولي للكتاب224065
معرّف المصادر الموحدhttp://dx.doi.org/10.1080/00224065.2018.1507561
معرّف المصادر الموحدhttp://hdl.handle.net/10576/13917
الملخصIn 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.
راعي المشروعThis 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.
اللغةen
الناشرTaylor and Francis Inc.
الموضوعBayesian statistics
data-driven
faulty variable identification
multivariate statistical process control
support vector data description
العنوانBayesian framework for fault variable identification
النوعArticle
الصفحات375-391
رقم العدد4
رقم المجلد51
dc.accessType Abstract Only


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