A new nonparametric double exponentially weighted moving average control chart

Raza, Muhammad Ali; Nawaz, Tahir; Aslam, Muhammad; Bhatti, Sajjad Haider; Sherwani, Rehan Ahmad Khan

doi:10.1002/qre.2560

Cited by 34 publications

(20 citation statements)

References 38 publications

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“…It showed that a fast initial response (FIR) feature is useful for the (EWMA) chart, especially for small values of smoothing constants. Raza [7] proposed a new (EWMA) control chart based on (SC) statistic that the area below the straight line is a control area that makes the control method simpler than other methods and can detect the source and direction of the change that occurs during the sampling period. Each of them found that this chart was more efficient than the Shewhart chart in detecting random deviations that occur during the production processes.…”

Section: Introductionmentioning

confidence: 99%

Charts for Exponentially Weighted Moving Average Classical and Robust: A Comparative Study

Saleh¹

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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This research discusses the comparison between two control charts classical & robust for both types (EWMA-SMQ) and(EWMA-SM) for the exponentially weighted moving average, which is showed that the robust (EWMA-SMQ) chart for the process is a superior alternative to the (EWMA-SM) chart when the outliers are present in the data. Generally, the (EWMA-SMQ) chart enables easier detection of outliers in the subgroups and is also more sensitive to other forms of out-of-control situations when outliers are present. Hence, the (EWMA-SMQ) becomes a preferred alternative to be taken and applied in quality control. The data used in this research represent the weights of the Al-Sabah Iraqi newspaper published by the Iraqi media networks. Twenty-five samples were taken and each sample consisted of five observations. These samples were taken at different production times, as the average weight of the newspaper was approximately ( 150) g. Through the application of the classical control chart (EWMA), exit four points for the upper and lower control limits, and the application of (EWMA-SM), (4) points are out of the upper and lower limits of the control; the control limits have been extended from the top and bottom sides of the (EWMA-SM) chart, making it less sensitive to the diagnosis of the shift in the mean process. The (EWMA-SMQ) chart detected extra points out of control. There were (7) points out of the upper and lower limits for the control, tight control limits of the (EWMA-SMQ) chart from the top and bottom sides. Due to this narrowness, three additional points were detected. Therefore (EWMA-SMQ) chart is more robust than the (EWMA-SM) control chart. This research aims to make a comparative study between the classical and robust for both types (EWMA-SM) & (EWMA-SMQ) for the exponentially weighted moving average control chart. The most important result of the research that the (EWMA-SMQ) chart is more suitable than the (EWMA-SM) & classical (EWMA) when outliers are present in the data. This gives importance to it to control the quality of the product. By this robust chart, the outliers must be detected, investigated and the special cause removed if possible. The presence of outliers will reduce the sensitivity of a control chart. The (EWMA-SMQ) control chart is more sensitive to out-of-control conditions when outliers are present in data. The limits computed from the estimate of the interquartile ranges for the (EWMA-SMQ) chart are less influenced by outliers than the (EWMA-SM) chart where the limits are computed based on the sample ranges. Thus the (EWMA-SMQ) chart is more robust than the (EWMA-SM) chart.

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Section: Introductionmentioning

confidence: 99%

Charts for Exponentially Weighted Moving Average Classical and Robust: A Comparative Study

Saleh¹

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…proposed a nonparametric EWMA chart with integer‐valued weights for monitoring the location parameter and it is seen its superiority against the CUSUM, EWMA, and GWMA sign charts in moderate to large shifts. Raza et al 19 . developed a nonparametric DEWMA chart based on the signed‐rank statistic to improve the performance of the EWMA signed‐rank chart.…”

Section: Introductionmentioning

confidence: 99%

A nonparametric triple exponentially weighted moving average sign control chart

Alevizakos

Chatterjee

Koukouvinos

2020

Quality & Reliability Eng

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Nonparametric control charts are used in process monitoring when there is insufficient information about the form of the underlying distribution. In this article, we propose a triple exponentially weighted moving average (TEWMA) control chart based on the sign statistic for monitoring the location parameter of an unknown continuous distribution. The run‐length characteristics of the proposed chart are evaluated performing Monte Carlo simulations. We also compare its statistical performance with existing nonparametric sign charts, such as the cumulative sum (CUSUM), exponentially weighted moving average (EWMA), generally weighted moving average (GWMA), and double exponentially weighted moving average (DEWMA) sign charts as well as the parametric TEWMA‐trueX¯ chart. The results show that the TEWMA sign chart is superior to its competitors, especially for small shifts. Moreover, two examples are given to demonstrate the application of the new scheme.

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“…Recently, Chen et al 30 developed the GWMA‐SN chart based on repetitive sampling (RS‐SWMA‐SN) to improve the performance of the GWMA‐SN chart in detecting small shifts. Raza et al 31 proposed the DEWMA signed‐rank (DEWMA‐SR) chart, and they showed that it outperforms the EWMA‐SN, EWMA‐SR, and DEWMA‐SN charts. Nonparametric GWMA and DGWMA charts were also developed in the case where the IC process median is unknown in advance (see Chakraborty et al 32 and Karakani et al 33 ).…”

Section: Introductionmentioning

confidence: 99%

A nonparametric double generally weighted moving average signed‐rank control chart for monitoring process location

Alevizakos

Koukouvinos

Chatterjee

2020

Quality & Reliability Eng

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Most control charts have been developed based on the actual distribution of the quality characteristic of interest. However, in many applications, there is a lack of knowledge about the process distribution. Therefore, in recent years, nonparametric (or distribution-free) control charts have been introduced for monitoring the process location or scale parameter. In this article, a nonparametric double generally weighted moving average control chart based on the signed-rank statistic (referred as DGWMA-SR chart) is proposed for monitoring the location parameter. We provide the exact approach to compute the run-length distribution, and through an extensive simulation study, we compare the performance of the proposed chart with existing nonparametric charts, such as the exponentially weighted moving average signed-rank (EWMA-SR), the generally weighted moving average signed-rank (GWMA-SR), the double exponentially weighted moving average signed-rank (DEWMA-SR), and the double generally weighted moving average sign (DGWMA-SN) charts, as well as the parametric DGWMA-X chart for subgroup averages. The simulation results show that the DGWMA-SR chart (with suitable parameters) is more sensitive than the other competing charts for small shifts in the location parameter and performs as well as the other nonparametric charts for larger shifts. Finally, two examples are given to illustrate the application of the proposed chart.

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A new nonparametric double exponentially weighted moving average control chart

Cited by 34 publications

References 38 publications

Charts for Exponentially Weighted Moving Average Classical and Robust: A Comparative Study

Charts for Exponentially Weighted Moving Average Classical and Robust: A Comparative Study

A nonparametric triple exponentially weighted moving average sign control chart

A nonparametric double generally weighted moving average signed‐rank control chart for monitoring process location

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