Design Strategies for the Multivariate Exponentially Weighted Moving Average Control Chart

Testik, Murat Caner; Borror, Connie M.

doi:10.1002/qre.568

Cited by 24 publications

(8 citation statements)

References 17 publications

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“…In general, there are several different approaches to the design of MEWMA control charts: (i) statistical design; (ii) economic-statistical design; and (iii) robust design. A review and a comparison of these design strategies is provided by Testik and Borror 114 . Yeh et al 115 gave a likelihood-ratio-based EWMA control chart that effectively monitors small changes of variability of multivariate normal processes.…”

Section: Mewma Chartsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

512

313

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In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewharttype control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.

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Section: Mewma Chartsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

512

313

View full text Add to dashboard Cite

show abstract

“…The multivariate exponentially weighted moving average (MEWMA) chart, as an extension of the conventional T 2 chart that has received extensive attention [30][31][32][33][34][35][36] , may further improve the monitoring performance for APC processes and merits further research.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

A Comparative Study of Joint Monitoring Schemes for APC Processes

Jiang

Shu

Tsung

2006

Quality & Reliability Eng

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To monitor processes under automatic process control (APC), several joint monitoring schemes have recently been proposed, such as the U statistics, U 0 and U ∞ , that originate from the uniformly most powerful test. It is known that the U 0 chart is sensitive to large shift detection whereas the U ∞ chart is sensitive to small shift detection. To take advantage of both charts, this paper examines a combined use of the two U charts. A comparative study indicates that the combined procedure improves over a single U chart and performs better than other joint monitoring schemes, including Hotelling's T 2 chart for APC processes.

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“…T 2 is an optimal testing statistic to detect whether the whole process is in out-of-control state under production modes with major fluctuations. Compared with T 2 control chart, MEWMA control chart shows its applicability in controlling multivariate quality indices and superiority in diagnosing small shifts in mean vector (Hawkins, Choi, and Lee 2007;Testik and Borror 2004;Yeh et al 2003). Thus, MEMWA control chart has been of great concern to process monitoring of various industries (Lee 2012;Mahmoud and Maravelakis 2010;Reynolds and Stoumbos 2008;Zhang, Li, and Wang 2010).…”

Section: Please Scroll Down For Articlementioning

confidence: 97%

Process monitoring research with various estimator-based MEWMA control charts

Miao

et al. 2015

International Journal of Production Research

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Multivariate exponentially weighted moving average (MEWMA) control chart with five different estimators as population covariance matrix is rarely applied to monitor small fluctuations in the statistical process control. In this article, mathematical models of the five estimators (S 1 , S 2 , S 3 , S 4 , S 5 ) are established, with which the relevant MEWMA control charts are obtained, respectively. Thereafter, the process monitoring performance of the five control charts is simulated. And the simulation results show that the S 4 estimator-based MEWMA control chart is of the best performance both in step offset failure mode and ramp offset failure mode. Since the inline process monitoring of photovoltaic manufacturing is intended to be a problem of multivariate statistics process analysis, the feasibility and effectiveness of the proposed model are elaborated in the case study during the cell testing and sorting process control for the fabrication of multicrystalline silicon solar cells.

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Design Strategies for the Multivariate Exponentially Weighted Moving Average Control Chart

Cited by 24 publications

References 17 publications

Multivariate statistical process control charts: an overview

Multivariate statistical process control charts: an overview

A Comparative Study of Joint Monitoring Schemes for APC Processes

Process monitoring research with various estimator-based MEWMA control charts

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