An adaptive thresholding-based process variability monitoring
Author | Abdella G.M. |
Author | Kim J. |
Author | Kim S. |
Author | Al-Khalifa K.N. |
Author | Jeong M.K.M.K. |
Author | Hamouda A.M. |
Author | Elsayed E.A. |
Available date | 2020-04-27T08:34:21Z |
Publication Date | 2019 |
Publication Name | Journal of Quality Technology |
Resource | Scopus |
ISSN | 224065 |
Abstract | In 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. |
Sponsor | This 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). |
Language | en |
Publisher | Taylor and Francis Inc. |
Subject | adaptive thresholding estimation monitoring covariance matrix multivariate statistical process control |
Type | Article |
Pagination | 242-256 |
Issue Number | 3 |
Volume Number | 51 |
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Mechanical & Industrial Engineering [1343 items ]