Design of a variable sampling interval exponentially weighted moving average median control chart in presence of measurement errors

Tran, Kim Duc; Nguyen, Huu Du; Nguyễn, Thị Hiền; Tran, Kim Phuc

doi:10.1002/qre.2726

Cited by 9 publications

(3 citation statements)

References 29 publications

(61 reference statements)

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“…A linear covariate error model was put forth by 1 when control charts with measurement error were taken into account. In recent years, numerous scholars have employed this paradigm extensively; see 2,3,4,5,6,7,8 . All of these studies showed that measurement error had a negative impact on the performances of control charts, making control charts perform worse.…”

Section: Introductionmentioning

confidence: 99%

The effect of measurement error on the performance of the s chart

Lee,

Teoh

et al. 2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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Section: Introductionmentioning

confidence: 99%

The effect of measurement error on the performance of the s chart

Lee,

Teoh

et al. 2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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“…Saccucci et al (1992) developed the VSI EWMA chart for the process mean, and they demonstrated that the developed chart outperforms the VSI CUSUM chart in detecting small shifts, as well as the VSI SH and non-adaptive EWMA charts in detecting all sizes of shifts. Recent studies on control charts with the VSI approach include those by Haq et al (2021), Tran et al (2021), Nguyen et al (2021), Umar et al (2021), Haq, Sadia, and Khoo (2022) and Haq and Khoo (2022a).…”

Section: Introductionmentioning

confidence: 99%

Proposed variable sampling interval maximum EWMA and distance EWMA charts with unknown process parameters

Parvin

Khoo

Saha

et al. 2023

Stat

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The variable sampling interval (VSI) exponentially weighted moving average (EWMA) chart which varies the chart's sampling interval according to the value of the current plotting statistic increases the speed of the standard EWMA chart in detecting shifts. Joint monitoring schemes use a single combined statistic for the mean and variance in process monitoring. To simultaneously monitor the mean and variance of a process from the normal distribution, two VSI EWMA schemes with unknown process parameters, based on (i) Maximum (Max) and (ii) Distance (Dis) type combining functions, are proposed in this paper. Each of these schemes uses a single plotting statistic. The effects of parameter estimation on the performance of the proposed VSI Max EWMA and VSI Dis EWMA schemes, in terms of the average time to signal, standard deviation of the time to signal, expected average time to signal and median time to signal criteria, are studied using Monte Carlo simulation. The results show that the proposed schemes can identify process shifts quicker than the existing Max/Dis Shewhart (SH), Max/Dis cumulative sum (CUSUM) and Max/Dis EWMA schemes. The implementation of the proposed schemes is demonstrated using a commercial dataset.

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“…Next, Maravelakis et al (2004) and Maravelakis (2012) investigated the effect of measurement errors on the EWMA and CUSUM schemes, respectively; with the effect of a two-component measurement error on the EWMA scheme investigated in Abbasi (2016). For some recent discussions on measurement errors published after the review paper of Maleki et al (2017), see for instance Asif et al (2020), Cheng and Wang (2018), Nguyen et al (2019), Noor-ul-Amin et al (2020), Riaz et al (2019), Sabahno et al (2019, 2020), Salmasnia et al (2018), Shongwe et al (2020a, b, c), Tang et al (2019), Tran et al (2019a, b, c, 2020), Yeong et al (2017), Zaidi et al (2019, 2020).…”

Section: Introductionmentioning

confidence: 99%

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

Thanwane

Malela‐Majika

Castagliola

et al. 2020

Transactions of the Institute of Measurement and Control

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Monitoring schemes are typically designed under the assumption of perfect measurements. However, in real-life applications, data tend to be subjected to measurement errors, that is, a difference between the real quantities and the measured ones mostly exist even with highly sophisticated advanced measuring instruments. Thus, in this paper, the negative effect of measurement errors on the performance of the homogenously weighted moving average (HWMA) scheme is studied using the linear covariate error model for constant and linearly increasing variance. Monte Carlo simulations are used to evaluate the performance of the proposed HWMA scheme in terms of the run-length characteristics. It is observed that as the smoothing parameter increases, measurement errors have a higher negative effect on the performance of the HWMA [Formula: see text] scheme. More importantly, it is shown that the negative effect of measurement errors is reduced by using multiple measurements and/or by increasing the slope coefficient of the covariate error model. Moreover, the performance of the HWMA [Formula: see text] scheme is compared with the corresponding exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) [Formula: see text] schemes. An illustrative example is provided to help in implementing this monitoring scheme in a real-life situation.

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Design of a variable sampling interval exponentially weighted moving average median control chart in presence of measurement errors

Cited by 9 publications

References 29 publications

The effect of measurement error on the performance of the s chart

The effect of measurement error on the performance of the s chart

Proposed variable sampling interval maximum EWMA and distance EWMA charts with unknown process parameters

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

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