The performance of double exponentially weighted moving average control charts for monitoring the coefficient of variation

Hu, Xuelong; Zhang, SuYing; Zhou, Xiaojian; Tang, Anan; Xie, FuPeng

doi:10.1080/03610918.2022.2057539

Cited by 5 publications

(6 citation statements)

References 24 publications

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“…As has been shown in Hu et al [13], among the three DEWMA-2 γ charts, the DEWMA3- 2 γ chart has the best performance when the shift size is moderate to large. When the shift size is small, the chart is inferior to the other two DEWMA-2 γ charts.…”

Section: The Dewma3-mentioning

confidence: 56%

“…First, we describe the properties of the sample statistic CV. Then, we briefly introduce some existing CV charts, including the EWMA-2 γ charts, the VSI EWMA- 2 γ charts and the three DEWMA-2 γ charts proposed in Hu et al [13].…”

Section: Some Existing CV Chartsmentioning

confidence: 99%

“…Other studies include investigating the performances of CUSUM CV charts with the presence of measurement errors in Tran et al [10], CV charts with run rules in Castagliola, Achouri, Taleb et al [10] , generally weighted moving average CV charts in Chen, Li and Zhang [11], and run-sum CV charts in Teoh, Khoo, Castagliola et al [12]. Recently, Hu, Zhang, Zhou et al [13] applied several double EWMA (DEWMA) schemes to monitor the CV squared in order to further increase the sensitivity of the EWMA CV chart to small shifts in the process. The numerical analysis demonstrated that the proposed one-sided DEWMA- 2 γ charts always have a better performance than the competing charts when the shift is very small.…”

Section: Introductionmentioning

confidence: 99%

“…In the light of the above literature review, this paper incorporates the VSI features to the three FSI DEWMA-2 γ charts proposed by Hu et al [13] so as to further improve their performances. The rest of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Coefficient of Variation Using Variable Sampling Interval Double Exponentially Weighted Moving Average Charts

Zhang

et al. 2021

Scientia Iranica

Self Cite

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As a measure of relative variability, the coefficient of variation (CV) is a valuable charting statistic in statistical process control. Great efforts have been devoted to monitoring CV efficiently. To further improve the performance of CV charts, this paper proposes three Double Exponentially Weighted Moving Average (DEWMA) charts by incorporating Variable Sampling Interval (VSI) strategies to monitor the CV squared. The run length properties of the proposed charts are evaluated via Monte Carlo simulations. Comparative studies show that the proposed VSI DEWMA CV charts detect the process shifts faster than the existing CV charts. A real data example is presented to illustrate the VSI DEWMA CV charts.

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Section: The Dewma3-mentioning

confidence: 56%

Section: Some Existing CV Chartsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring Coefficient of Variation Using Variable Sampling Interval Double Exponentially Weighted Moving Average Charts

Zhang

et al. 2021

Scientia Iranica

Self Cite

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“…For CV charts that do not fall in any of these categories, they are classified under the “others” category. The EWMA‐based CV charts were proposed by Hong et al., 2,3 Castagliola et al., 4 Zhang et al., 5 Yeong et al., 6 Muhammad et al., 7 Zhang et al., 8 Haq and Khoo, 9 Teoh et al., 10 Arshad et al., 11 and Hu et al 12 . CV charts that are based on the CUSUM approach were suggested by Lee et al., 13,14 Tran and Heuchenne, 15 Tran et al., 16 and Tran and Heuchenne 17 .…”

Section: Introductionmentioning

confidence: 99%

A near‐optimal CUSUM‐CV chart

Haq

Khoo

2023

Quality & Reliability Eng

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Monitoring of the process coefficient of variation (CV) with one‐sided CUSUM charts has been considered in the literature. In this short note, we develop a near‐optimal one‐sided CUSUM chart (with an explicit mathematical expression of the reference parameter) for detecting either increases or decreases in the CV of a normally distributed process. It is shown that the run‐length profiles of the existing and proposed one‐sided CUSUM‐CV charts (despite the fact that both charts possess different control‐charting structures) approximately remain the same. However, the proposed one‐sided CUSUM‐CV charts can be easily optimized when detecting a specific upward or downward shift in the in‐control process CV.

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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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The performance of double exponentially weighted moving average control charts for monitoring the coefficient of variation

Cited by 5 publications

References 24 publications

Monitoring Coefficient of Variation Using Variable Sampling Interval Double Exponentially Weighted Moving Average Charts

Monitoring Coefficient of Variation Using Variable Sampling Interval Double Exponentially Weighted Moving Average Charts

A near‐optimal CUSUM‐CV chart

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

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