Bayesian framework for fault variable identification
المؤلف | Turkoz M. |
المؤلف | Kim S. |
المؤلف | Jeong Y.-S. |
المؤلف | Jeong M.K. |
المؤلف | Elsayed E.A. |
المؤلف | Al-Khalifa K.N. |
المؤلف | Hamouda A.M. |
تاريخ الإتاحة | 2020-04-09T07:35:00Z |
تاريخ النشر | 2019 |
اسم المنشور | Journal of Quality Technology |
المصدر | Scopus |
الرقم المعياري الدولي للكتاب | 224065 |
الملخص | In 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 المشروع | This 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. |
اللغة | en |
الناشر | Taylor and Francis Inc. |
الموضوع | Bayesian statistics data-driven faulty variable identification multivariate statistical process control support vector data description |
النوع | Article |
الصفحات | 375-391 |
رقم العدد | 4 |
رقم المجلد | 51 |
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