EWMA Chart and Measurement Error

Maravelakis, Petros E.; Panaretos, John; Psarakis, Stelios

doi:10.1080/02664760410001681738

Cited by 85 publications

(77 citation statements)

References 18 publications

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“…The results reveal that as the ratio σ 2 m /σ 2 increases, the CUSUM charts need more data (time) to detect a shift than in the no error case. Similar results were reported by Linna and Woodall [11] as well as Maravelakis et al [15].…”

Section: Effect Of Measurement Errorsupporting

confidence: 88%

“…They considered the EWMA-X-S chart which is a combination of the EWMA charts for the mean and the standard deviation. Maravelakis et al [15] examined the case of the EWMA control chart for the mean assuming the model introduced in Linna and Woodall [11]. Shore [20] dealt with the problem of designing Shewhart control charts under the presence of measurement error.…”

Section: Introductionmentioning

confidence: 98%

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Measurement error effect on the CUSUM control chart

Maravelakis¹

2012

Journal of Applied Statistics

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The performance of the cumulative sum (CUSUM) control chart for the mean when measurement error exists is investigated. It is shown that the CUSUM chart is greatly affected by the measurement error. A similar result holds for the case of the CUSUM chart for the mean with linearly increasing variance. In this paper, we consider multiple measurements to reduce the effect of measurement error on the charts performance. Finally, a comparison of the CUSUM and EWMA charts is presented and certain recommendations are given.

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Section: Effect Of Measurement Errorsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 98%

Measurement error effect on the CUSUM control chart

Maravelakis¹

2012

Journal of Applied Statistics

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“…But, in many industrial scenarios, there often exist significant measurement errors that affect the performance of control charts. Since Bennet (1954) investigated the effect of measurement errors on the ShewhartX chart, there are many studies about the actual performance of various control charts in the presence of measurement error, for further details see for instance: Kanazuka (1986), , Linna, Woodall, and Busby (2001), Maravelakis (2004), Costa and Castagliola (2011), Maravelakis (2012). Recently, Hu et al (2015) investigated the performance of the syntheticX chart when measurement errors exist using a linear covariate error model and they showed that the performance of the syntheticX chart is significantly affected in the presence of measurement error.…”

Section: Introductionmentioning

confidence: 99%

The performance of the Shewhart-RZ control chart in the presence of measurement error

Tran

Castagliola

Celano

2016

International Journal of Production Research

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Recent literature about quality control has investigated the continuous surveillance of the ratio of two normal random variables under the assumption of no measurement error. However, in practice, measurement errors always exist in quality control applications and may considerably affect the performance of control charts. In this paper, the performance of the Shewhart-RZ control chart is investigated in the presence of a measurement error and modelled by a linear covariate error model. Several figures and tables are generated and commented to show the statistical performance of the Shewhart-RZ control chart for different sources of the measurement error. Two examples illustrate the use of this chart on a quality control problem simulated from the food industry and a real industrial case from a plant treating batteries for recyclement.

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“…Further researches are in progress for evalauting the efficiency of other types of multivariate control charts (Multivariate Cumulative Sum [11] and Multivariate Exponentially Weighted Moving Average [12]) for monitoring small and moderate shifts in the mean vector. …”

Section: Discussionmentioning

confidence: 99%

Multivariate Statistical Process Control of Electrostatic Separation Processes

Senouci

Bendaoud

Tilmatine

et al. 2007

2007 IEEE Industry Applications Annual Meeting

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Multivariate control charts are commonly used for monitoring processes the quality of which is determined by two or more correlated output variables. The aim of this paper is to point out the effectiveness of multicriterion control charts for supervising the variability of the outcome of an electrostatic separation process. The experiments were carried out on samples of chopped electric cable wastes, similar to those currently processed by the recycling industry. The two output variables considered in the study were the masses of product recovered in the middling and conductive compartments of the collector. When the separation process was under control, the two variables were correlated, and T2-type control charts could be established. Two out-of-control situations were simulated. The multivariable control charts monitoring the process location and spread were able to detect these situations, though each output variable taken independently remained within the control limits.

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EWMA Chart and Measurement Error

Abstract: A Measurement error is a usually met distortion factor in real-

Cited by 85 publications

References 18 publications

Measurement error effect on the CUSUM control chart

Measurement error effect on the CUSUM control chart

The performance of the Shewhart-RZ control chart in the presence of measurement error

Multivariate Statistical Process Control of Electrostatic Separation Processes

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