A New Distribution-Free Control Chart for Monitoring Process Median Based on the Statistic of the Sign Test

Ali, Saber; Abbas, Zameer; Nazir, Hafız Zafar; Riaz, Muhammad; Abid, Muhammad

doi:10.1520/jte20210135

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…At the same time, their findings showed that the proposed chart was better than existing control charts in identifying small shifts in process location. The effectiveness of a distribution-free double-exponentially weighted moving average chart based on the sign test statistic was compared with the output of existing control charts employing run length tests [13]. The assessment revealed that the proposed chart outperforms its competitors in detecting small and medium shifts in process location.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean

Talordphop,

Areepong,

Sukparungsee

2023

Mathematics

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In the real world, a nonparametric control chart is a powerful substitute for enhancing outcome quality, although the fundamental procedure characteristic often fails to match the distribution assumptions. This study aims to construct and evaluate an extended exponentially weighted moving average control chart based on signed-rank statistics (EEWMA-SR) for recognizing changes in procedures. According to the study, the proposed chart proves more potent recognition of shifts in the process mean, predominantly small shifts, than the other control charts by the average run length in Monte Carlo simulation. Applying the proposed control chart to an actual dataset yielded events that corroborated the study discoveries.

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Section: Introductionmentioning

confidence: 99%

Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean

Talordphop,

Areepong,

Sukparungsee

2023

Mathematics

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“…Thus, control charts have better performance if they identify the shifts in the process when it is out‐of‐control (

\mathrm{OOC}

) and generate fewer false. Ali et al 4 . examine that the parametric control charts produce more false alarms and unacceptable OOC signals when the underlying distribution of the process is not normal.…”

Section: Introductionmentioning

confidence: 99%

Multivariate cumulative sum control chart for compositional data with known and estimated process parameters

Imran

Sun

Zaidi

et al. 2022

Quality & Reliability Eng

Self Cite

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This article uses the classic multivariate cumulative sum (MCUSUM$\mathrm{MCUSUM}$) chart scheme proposed by Crossier (1988) to present a new modified MCUSUM$\mathrm{MCUSUM}$ chart for compositional data (CoDa$\mathrm{CoDa}$). For this purpose, the data are first transformed using isometric log‐ratio (ilr$\operatorname{ilr}$) coordinates representation to eliminate the constant sum constraint of CoDa$\mathrm{CoDa}$. The MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ control chart has been defined along with the performance measures of the proposed chart using the average run length (ARL$\mathrm{ARL}$). Besides, the Markov chain method has been used to study the ARL$\mathrm{ARL}$ performance of the proposed chart. Assuming that the ilr$\operatorname{ilr}$ transformed data are normally distributed, the proposed MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ charts have been compared with existing competitors such as T2$T^2$‐CoDa$\mathrm{CoDa}$ and MEWMA$\mathrm{MEWMA}$‐CoDa$\mathrm{CoDa}$ charts. The comparison shows that the proposed chart has better performance than the T2$T^2$‐CoDa$\mathrm{CoDa}$ control charts, while the performance of the proposed chart is comparable with the MEWMA$\mathrm{MEWMA}$‐CoDa$\mathrm{CoDa}$ chart. The effect of the estimated mean vector and variance‐co‐variance matrix on run‐length characteristics of the proposed MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ control chart has also been studied in this paper. For the ARL$\mathrm{ARL}$ performance of MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ with estimated parameters Monte Carlo simulation has been adopted. The effect of the number of variables p$p$, sample size n$n$, and subgroup size m$m$ has also been studied on the data's upper control limit (UCL$\mathrm{UCL}$) and ARL$\mathrm{ARL}$. In the end, two illustrative examples of the particle size distribution of plants and production of muesli are provided to represent the practical implementation of the MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ chart.

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“…proposed triple EWMA sign chart. Furthermore, the works on improving sign charts include the works of Yang, 20 Chen et al., 21 Castagliola et al., 22 Aslam et al., 23 Castagliola et al., 24 Riaz et al., 25 Abbas et al., 26 Mahadik and Godase, 27 Ali et al., 28 and Khatun et al 29 …”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Riaz and Abbasi 18 proposed double EWMA sign chart and Alevizakos et al 19 proposed triple EWMA sign chart. Furthermore, the works on improving sign charts include the works of Yang, 20 Chen et al, 21 Castagliola et al, 22 Aslam et al, 23 Castagliola et al, 24 Riaz et al, 25 Abbas et al, 26 Mahadik and Godase, 27 Ali et al, 28 and Khatun et al 29 All the above mentioned sign-based control charts are suitable for detecting shifts in process location. However, the nonparametric monitoring of process dispersion is also highly desirable.…”

Section: Introductionmentioning

confidence: 99%

Deciles‐based EWMA‐type sign charts for process dispersion

Godase

Rakitzis

Mahadik

et al. 2022

Quality & Reliability Eng

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In this paper, we propose and study nonparametric exponentially weighted moving average (EWMA) control charts based on the sign statistic, which are suitable for detecting changes in process dispersion. The sign statistic is defined by using appropriate deciles of the in‐control process distribution. The statistical performances of the proposed charts are determined through Monte Carlo simulation. Practical guidelines are provided for the statistical designs of these charts. We evaluate the statistical performances of the proposed charts by comparing them with that of the deciles‐based CUSUM sign chart and the EWMA lnS2 chart. The comparisons show that the proposed charts are viable nonparametric methods for monitoring the process dispersion. Finally, we provide an illustrative example to demonstrate the implementation of the proposed charts in practice.

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A New Distribution-Free Control Chart for Monitoring Process Median Based on the Statistic of the Sign Test

Cited by 5 publications

References 33 publications

Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean

Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean

Multivariate cumulative sum control chart for compositional data with known and estimated process parameters

Deciles‐based EWMA‐type sign charts for process dispersion

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