A nonparametric control chart based on the Mood statistic for dispersion

Murakami, Hidetoshi; Matsuki, Takashi

doi:10.1007/s00170-009-2439-3

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…In addition to the previous three classes of nonparametric methods, the fourth class of NPCC defined in [10] covers various special cases that were introduced for instance in [15,21,[39][40][41]. Adekeye and Azubuike in [15] compared the classical Shewhart X-bar control chart with the MAD (median absolute deviation) control chart.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes

Smajdorová

Noskievičová

2022

Applied Sciences

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Nonparametric control charts (NPCC) have shown great potential for monitoring processes in conditions of smart manufacturing with complex structures, various monitored characteristics and the need to process big data. Practical applications of NPCCs are very rare. The main reasons for this situation are a deficiency in software support and a lack of simple but complete instructions for their application. The introduction of such manual, which is based on the authors’ own simulations of performance of wide spectrum of NPCCs in conditions of different violations of data prerequisites, leading to recommendations for the selection of the most effective NPCC in various practical situations, is the main goal of this paper. Compared to other similar studies, this approach covers a wider range of control charts, and it was applied to a wider spectrum of data assumption violations. As an integral part of these analyses, an examination of various control chart performance indicators such as ARL, MRL, x5 and x95 was performed using simulations to select the best of them. The designed methodology was verified using real data.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes

Smajdorová

Noskievičová

2022

Applied Sciences

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“…x Target short run In spite of this practical situation there has been written a lot of papers and articles relating to nonparametric control charts (see for instance Chakraborti et al, 2001;Das, 2008;Chakraborti and Van de Wiel, 2008;Bush et al, 2010;Murakami and Matsuki, 2010;Graham et al, 2011;Yang et al, 2011;Lu, 2015;Oprime et al, 2016;Wang et al, 2016). Due to the nonparametric methods properties this gap between practice and theory must be removed and nonparametric methods must be made available to practitioners to stay the obvious methods for effective statistical process monitoring especially in the phase I.…”

Section: Type Of Control Chartmentioning

confidence: 99%

Proposal of Methodology for Practical Application of Nonparametric Control Charts

Noskievičová

Smajdorová

2019

Quality Production Improvement - QPI

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This paper deals with the methodology for practical application of nonparametric control charts. This topic is very important for two reasons: firstly nonparametric control charts are very effective instruments for the realization of the statistical process monitoring phase I due to their robustness against various deviations from the data assumptions that must be met when applying model-based control charts. Secondly nonparametric control charts have very weak SW support and also they are not taught in the frame of training courses not even of the university study programmes. For that reason the practitioners do not know them and do not use them. The paper offers the proposal how to practically apply these control charts which is based on the complex simulation study of various nonparametric control charts performance when various data assumptions have not been met. The study has covered these nonparametric control charts: Shewhart sign control chart, nonparametric EWMA and nonparametric CUSUM control charts, nonparametric progressive mean control chart, control chart based on Mood statistics and robust median absolute deviation control chart. All charts have been studied in condition of not normally distributed data, autocorrelated data and data with nonconstant distribution parameters. The simulations were realized for statistically stable (IC – in control) and also statistically unstable (OC – out of control) processes. For the evaluation of the control charts performance median run length, 0.05-quantile, and 0.95-quantile were used.

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“…studied statistically designed NSPM schemes using the AB statistic. Murakami and Matsuki, 29 Villanueva‐Guerra et al., 30 Mukherjee and Marozzi 31 considered the Mood statistic to capture process variability. These studies paid attention to the statistical performance of NSPM schemes without mentioning about the cost aspect.…”

Section: Introductionmentioning

confidence: 99%

Two economically optimized nonparametric schemes for monitoring process variability

Mukherjee

2021

Quality & Reliability Eng

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Controlling and reducing process variability is an essential aspect for maintaining the product or service quality. Even though most practitioners believe that an increasing process variability is often a more severe concern than a shift in location, barely a few research paid attention to the cost‐efficient monitoring of process variability. Some of the existing studies addressed the dispersion aspect, assuming that the quality characteristic is Gaussian. Non‐normal and complex distributions are not uncommon in modern production processes, time to event processes, or processes involving service quality. Unfortunately, we find no literature on economically designed nonparametric (distribution‐free) schemes for monitoring process variability. This article introduces two Shewhart‐type cost‐optimized nonparametric schemes for monitoring the variability of any unknown but continuous processes to fill the research gap. The proposed monitoring schemes are based on two popular two‐sample rank statistics for differences in scale parameters, known as the Ansari–Bradley statistic and the Mood statistic. We assess their actual performance for a set of process scenarios and illustrate the design along with the implementation steps. We discuss a practical problem related to product quality management. It is expected that the proposed schemes will be beneficial in various industrial operations.

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A nonparametric control chart based on the Mood statistic for dispersion

Cited by 14 publications

References 11 publications

Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes

Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes

Proposal of Methodology for Practical Application of Nonparametric Control Charts

Two economically optimized nonparametric schemes for monitoring process variability

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