A Nonparametric EWMA Sign Chart for Location Based on Individual Measurements

Graham, Marien Alet; Chakraborti, S.; Human, Schalk William

doi:10.1080/08982112.2011.575745

Cited by 82 publications

(65 citation statements)

References 37 publications

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“…Li et al . proposed NPEWMA and NPCUSUM charts on the basis of the Mann–Whitney statistics, Zou and Tsung proposed a multivariate EWMA control chart using the weighted version of sign test, Graham et al . proposed NPEWMA sign chart for monitoring process location using individual observations, Yang et al .…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Abbasi

Miller

Riaz

2012

Quality & Reliability Eng

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Nonparametric control charts are widely used when the parametric distribution of the quality characteristic of interest is questionable. In this study, we proposed a nonparametric progressive mean control chart, namely the nonparametric progressive mean chart, for efficient detection of disturbances in process location or target. The proposed chart is compared with the recently proposed nonparametric exponentially weighted moving average and nonparametric cumulative sum charts using different run length characteristics such as the average run length, standard deviation of the run length, and the percentile points of the run length distribution. The comparisons revealed that the proposed chart outperformed recent nonparametric exponentially weighted moving average and nonparametric cumulative sum charts, in terms of detecting the shifts in process target. A real life example concerning the fill heights of soft drink beverage bottles is also provided to illustrate the application of the proposed nonparametric control chart. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Abbasi

Miller

Riaz

2012

Quality & Reliability Eng

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“…As in Graham et al [12], the Student's and Gamma distributions were shifted and/or scaled, as necessary, in order to make the in-control process mean μ 0 = 0 and the standard deviation equal to σ 0 = 1, respectively. This is so that the results are comparable across distributions.…”

Section: Performance Studymentioning

confidence: 99%

The performance of the Shewhart sign control chart for finite horizon processes

Celano

Castagliola

Chakraborti

et al. 2015

Int J Adv Manuf Technol

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In many manufacturing environments, the production horizon of the same part code between two consecutive set-ups should be limited to a few hours or shifts. When 100 % sampling is not possible, on-line quality control on a quality characteristic should be immediately started by means of a control chart. In this paper, we investigate the statistical performance of a nonparametric (distributionfree) Shewhart Sign (SN) control chart for monitoring the location of a quality characteristic in a production process with a finite horizon and a small number of scheduled inspections. The observations taken from the process are assumed to be continuous random variables. By implementing a SN control chart, any model assumption about the distribution of observations is needless to guarantee a nominal in-control (IC) performance: after each process set-up, this overcomes the important problem of lack of information about the distribution of the observations collected for the quality characteristic to be monitored. An extensive simulation study is conducted to compare the statistical performance of the distribution-free Shewhart SN control chart to the normal theory-based Shewhart Student's t control chart: several types of distributions of observations and different numbers of scheduled inspections are considered to show the advantages related to the implementation of the Shewhart SN control chart. An illustrative example presents the implementation of the Shewhart SN control chart on a real data set collected in a beverage company.

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“…Afterwards, many works have been contributed on small process shifts when the process distribution is unknown. (see, Yang and Cheng [11]; Yang et al [12]; Graham et al [13]). …”

Section: Intropductionmentioning

confidence: 96%

An Intelligent Negotiation Model for Quality of Service Chain

Tsai

2014

2014 IEEE 11th International Conference on E-Business Engineering

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The cloud computing and Service-oriented architecture (SOA) assist and partake for cost-effective improvement of cloud service suppliers in recent years. This paper describes a new grey forecasting which optimize the negotiation process of adaptive Quality of Service (QoS). This study designs a generally weighted moving average (GWMA) sign chart for quality of service chain. A group of suppliers and consumers faced with multiple negotiations related to dynamic services exited both-side benefits conflicting. In a complicated and challenging negotiation procedure to produce multiple acceptable alternatives by both-side preferences. The intelligent service ranking controller assists planners to optimize generalized favourite agreement over the negotiations. The contribution of the research is to present that the alternatives of favourite ranking sequence by a GWMA-adaptive genetic algorithm (AGA) negotiation approach can be significant to improve in the quality of service chain. Finally, an experimental simulation is presented to verify this research.

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A Nonparametric EWMA Sign Chart for Location Based on Individual Measurements

Cited by 82 publications

References 37 publications

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Nonparametric Progressive Mean Control Chart for Monitoring Process Target

The performance of the Shewhart sign control chart for finite horizon processes

An Intelligent Negotiation Model for Quality of Service Chain

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