A fuzzy nonparametric Shewhart chart based on the bootstrap approach

Wang, Dabuxilatu; Hryniewicz, Olgierd

doi:10.1515/amcs-2015-0030

Cited by 28 publications

(14 citation statements)

References 36 publications

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“…Therefore, an alternative to the existing chart is the use of a control chart using the fuzzy approach. A piece of detailed information about the fuzzy control charts can be seen in references [10][11][12][13][14][15][16] .…”

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confidence: 99%

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Aslam

Shareef

Khan

2020

Sci Rep

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the existing moving average control charts can be only applied when all observations in the data are determined, precise, and certain. But, in practice, the data from the weather monitoring is not exact and express in the interval. in this situation, the available monitoring plans cannot be applied for the monitoring of weather data. A new moving average control chart for the normal distribution is offered under the neutrosophic statistics. The parameters of the offered chart are determined through simulation under neutrosophic statistics. the comparison study shows the superiority of the proposed chart over the moving average control chart under classical statistics. A real example from the weather is chosen to present the implementation of the chart. from the simulation study and real data, the proposed chart is found to be effective to be applied for temperature monitoring than the existing control chart.

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confidence: 99%

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Aslam

Shareef

Khan

2020

Sci Rep

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“…Shu, Dang, Nguyen, Hsu & Phan (2017) proposed fuzzy control limits based on results of the resolution identity in fuzzy set theory. Soleymani & Amiri, (2017) Many other researchers have contributed to fuzzy process control works from different point of view including skewed data in fuzzy control charts (Atta, Shoraim, Yahaya, Zain & Ali, 2016;Yimnak & Intaramo, 2020), nonparametric fuzzy charts (Momeni & Shokri, 2019;Wang & Hryniewicz, 2015), flexible control charts (Pekin Alakoc & Apaydin, 2018), economic design of individual control chart (Wang & Chen, 1995;Chen, Chang & Chiu, 2008), fuzzy inference control system (Saricicek & Cimen, 2011), charts for auto correlated fuzzy observations (Sadeghpour Gildeh & Shafiee, 2015), performance of FEV theory control charts with αcut level fuzzy midrange method for three skewed distributions (Intaramo, 2012), nonrandom patterns of fuzzy control charts and fuzzy run rules (Hsu & Chen, 2001;Tannock, 2003;Gulbay & Kahraman, 2006;Chih & Kuo, 2007;Fazel Zarandi, Alaeddini & Turksen 2008;Demirli & Vijayakumar, 2010;Pekin Alakoc & Apaydin, 2013), detecting mean and variance shifts of a process (Chang & Aw, 1996;Moameni, Saghaei, & Ghorbani Salnghooch, 2012;Salnghooch, 2015;Kaya, Erdogan & Yildiz, 2017), fuzzy multivariate control charts (Taleb Limam & Hirota, 2006;Moheb Alizadeh, Arshadi Khamseh & Fatemi Ghomi, 2010;Pastuizaca Fernandez, Carrion Garcia, A. & Ruiz Barzola, 2015), multi objective design of control charts (Morabi, Owlia, Bashiri & Doroudyan, 2015), fuzzy CUSUM and EWMA control charts (Senturk, Erginel, Kaya, & Kahraman, 2014;Akhundjanov & Pascual, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

Alakoc

2021

Journal of Advanced Research in Natural and Applied Sciences

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There have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variability, which leads to less cost, production time, number of defects, scrap, rework and more customer satisfaction. Quality can be improved by reducing product variability. On the other hand, uncertainty or subjectivity is a part of many engineering and real life problems. However, these problems cannot be solved by traditional methods. This study focuses on constructing Xbar and S control charts in fuzzy environment. The approach is developed by considering the theoretical structure of the Shewhart control charts. The core of the approach depends on the combination of parametric interval estimation and fuzzy statistics. Control limits and samples are presented by fuzzy numbers which ensures to maintain fuzziness in control charts. An important property of the approach is that the fuzzy charts can be reduced to Shewhart control charts. A simulation study was conducted for the performance evaluation of fuzzy Xbar and S control charts. The proposed fuzzy control chart is sensitive to process mean shifts and variance changes, and outperforms the traditional control charts under the changes of variance. In addition, an example from the literature shows that the approach is an effective way of presenting fuzziness in the quality characteristics, which enables the approach to have high applicability to the real life problems.

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“…The fuzzy-based control charts are the best alternative to monitor the process when observations or the parameters under study are fuzzy. As mentioned by Khademi and Amirzadeh [20], "Fuzzy data exist ubiquitously in the modern manufacturing process"; therefore, serval authors paid attention to work on such control charts, such as for example [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average

2019

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The existing Shewhart X-bar control charts using the exponentially weighted moving average statistic are designed under the assumption that all observations are precise, determined, and known. In practice, it may be possible that the sample or the population observations are imprecise or fuzzy. In this paper, we present the designing of the X-bar control chart under the symmetry property of normal distribution using the neutrosophic exponentially weighted moving average statistics. We will first introduce the neutrosophic exponentially weighted moving average statistic, and then use it to design the X-bar control chart for monitoring the data under an uncertainty environment. We will determine the neutrosophic average run length using the neutrosophic Monte Carlo simulation. The efficiency of the proposed plan will be compared with existing control charts.

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A fuzzy nonparametric Shewhart chart based on the bootstrap approach

Cited by 28 publications

References 36 publications

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

RETRACTED ARTICLE: Monitoring the temperature through moving average control under uncertainty environment

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average

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