On developing an exponentially weighted moving average chart under progressive setup: An efficient approach to manufacturing processes

Abbas, Zameer; Nazir, Hafız Zafar; Akhtar, Noureen; Riaz, Muhammad; Abid, Muhammad

doi:10.1002/qre.2716

Cited by 25 publications

(23 citation statements)

References 34 publications

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“…To evaluate the sensitivity of the proposed -chart the notions of the neutrosophic operating characteristic curve ( ), ARL and PC are described. There are widely used metrics to assess the efficiency of proposed design in classical control charts theory [28]. The and functions are customary employed to characterize the control chart's ability to detect a difference in the observed quality phase.…”

Section: Performance Analysismentioning

confidence: 99%

Neutrosophic Rayleigh Model With Some Basic Characteristics and Engineering Applications

et al. 2021

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The fundamentals of neutrosophic statistics provide a new basis for working with indeterminate data problems. In this study, the notion of the neutrosophic Rayleigh distribution () has been introduced. The neutrosphic extension of the classical Rayleigh model with several application areas is highlighted. The major characteristics of the proposed distribution are described in a way that suggested model can be utilized in different situations involving undetermined, vague and fuzzy data. The usage of proposed distribution notably in the domain of statistical process control () is considered. The classical structure of -chart is not capable of capturing uncertainty on studied variables. The mathematical structure of the -chart based on the proposed neutrosophic distribution has been developed. The neutrosphic parameters of the proposed -chart with other related performance metrics such as neutrosophy run length () and neutrosophy power curve ( ) are established. The proposed chart's performance in a neutrosophic environment is also evaluated to the existing model. Results from this comparative analysis reveal that the suggested Vchart outperforms its current equivalent in terms of neutrosophic statistical power. Finally, a charting structure of proposed design for service life of ball bearings data is considered with a view to support implementation procedure of the proposed neutrosophic design in real-world scenarios.

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Section: Performance Analysismentioning

confidence: 99%

Neutrosophic Rayleigh Model With Some Basic Characteristics and Engineering Applications

et al. 2021

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“…Where E F is a constant whose value depends on the sample size (n), it is shown in table (1). The standard deviation ( ( ) is estimated by:…”

Section: The Robust (Ewma-smq) Chartmentioning

confidence: 99%

“…Power control charts lie in their ability to diagnose the shift in processes and to distinguish abnormal situations. Some previous studies designed the first control chart, and several attempts were appeared to develop what began Shewhart [1], [2]. The focus was on what goes wrong on Shewhart's chart that it is less sensitive in detecting of small continuous and medium changes the average process changed, and try to reduce the limits of control to less than three standard deviations from the use of scientific methods.…”

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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“…Similar results as regard as the robustness with those observed for the TEWMA‐TBE chart can be seen for each control chart individually. In order to compare the robustness of the competing charts, we use the relative percentage change 38 that is defined as

Relative percentage change = (\frac{A R L_{W} - A R L_{E}}{A R L_{E}}) \times 100 %,

where

A R L_{W}

and

A R L_{E}

are the ARL values under the Weibull and exponential distributions, respectively. A control chart with a relative percentage value close to zero is considered to be very robust.…”

Section: Robustness Studymentioning

confidence: 99%

A triple exponentially weighted moving average control chart for monitoring time between events

Alevizakos

Chatterjee

Koukouvinos

2020

Quality & Reliability Eng

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Time between events (TBE) charts are used in high‐yield processes where the rate of occurrences is very low. In the current article, we propose a triple exponentially weighted moving average control chart to monitor TBE (regarded as triple exponentially weighted moving average TEWMA‐TBE chart) modeled by a gamma distribution. One‐ and two‐sided schemes of the proposed chart are designed and compared with the double EWMA DEWMA‐TBE and EWMA‐TBE charts. It is shown that the lower‐ and two‐sided TEWMA‐TBE charts outperform its competitors, especially for small to moderate downward shifts, while the upper‐sided TEWMA‐TBE chart has very good detection ability for small shifts. We also study the robustness of the proposed chart when the true distribution is a Weibull or a lognormal and it is found that the TEWMA‐TBE chart has better robustness properties than its competitors, especially for small shifts. Two illustrative examples from airplane accidents and earthquakes are also provided to display the application of the proposed chart.

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On developing an exponentially weighted moving average chart under progressive setup: An efficient approach to manufacturing processes

Cited by 25 publications

References 34 publications

Neutrosophic Rayleigh Model With Some Basic Characteristics and Engineering Applications

Neutrosophic Rayleigh Model With Some Basic Characteristics and Engineering Applications

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

A triple exponentially weighted moving average control chart for monitoring time between events

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