Optimal design of a synthetic chart for monitoring process dispersion with unknown in-control variance

Guo, Baining; Wang, Bingxing; Cheng, Yaqi

doi:10.1016/j.cie.2015.06.024

Cited by 14 publications

(11 citation statements)

References 45 publications

(54 reference statements)

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“…Numerous works on control charts have been conducted over the years which have contributed to the area of SPC. Some of the recent ones were made by Ali and Riaz (2014), Teoh, Khoo, Castagliola, and Chakraborti (2014), Zhang, Li, Chen, and Wang (2014), Guo, Wang, and Cheng (2015), Nenes, Tasias, and Celano (2015) and Noorossana, Shekary, and Deheshvar (2015).…”

Section: Introductionmentioning

confidence: 97%

The variable sampling interval run sumX‾control chart

Chew

Khoo

Teh

et al. 2015

Computers & Industrial Engineering

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Section: Introductionmentioning

confidence: 97%

The variable sampling interval run sumX‾control chart

Chew

Khoo

Teh

et al. 2015

Computers & Industrial Engineering

View full text Add to dashboard Cite

“…The effect of non‐normality on the Downton's estimator was also examined. Recently, Guo et al studied a S1 chart based on the S 2 statistic, in both case K and case U. Finally, a S1 DS S chart was recently studied by Lee and Khoo .…”

Section: Literature Reviewmentioning

confidence: 99%

“…A corresponding economic design of a slightly modified S1 DS

\overset{X}{true}

chart was done by Aghaulor and Ezekwem . In the case of monitoring process variation, the economic design of the upper one‐sided chart of Guo et al in the presence of measurement errors was studied by Hu et al Lee and Khoo provided the economic‐statistical design of the S1 Max chart of Chen and Huang . Finally, Wan et al studied the integrated economic design of the adaptive IS1 chart of Yu et al and the maintenance management system.…”

Section: Literature Reviewmentioning

confidence: 99%

An overview of synthetic‐type control charts: Techniques and methodology

Rakitzis

Chakraborti

Shongwe

et al. 2019

Quality & Reliability Eng

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In this paper, we provide an overview of a class of control charts called the synthetic charts. Synthetic charts are a combination of a traditional chart (such as a Shewhart, CUSUM, or EWMA chart) and a conforming run‐length (CRL) chart. These charts have been considered in order to maintain the simplicity and improve the performance of small and medium‐sized shift detection of the traditional Shewhart charts. We distinguish between different types of synthetic‐type charts currently available in the literature and highlight how each is designed and implemented in practice. More than 100 publications on univariate and multivariate synthetic‐type charts are reviewed here. We end with some concluding remarks and a list of some future research ideas.

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“…This has caused some studies on the synthetic charts with unknown process parameters. Zhang et al and Guo and Wang have investigated the performance of the synthetic charts for monitoring the process mean and variance, respectively. On the basis of the average run length (

A R L

), the average number of observations to signal (

A N O S

), and the standard deviation of the run length (

S D R L

), You et al investigated the performance of the synthetic double sampling (SDS)

\overset{X}{true}

chart with unknown process parameters.…”

Section: Introductionmentioning

confidence: 99%

Conditional median run length performance of the synthetic X¯ chart with unknown process parameters

Castagliola

Tang

et al. 2020

Quality & Reliability Eng

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Synthetic‐type charts are efficient tools for process monitoring. They are easy to design and implement in practice. The properties of these charts are usually evaluated under the assumption of known process parameters. This assumption is sometimes violated in practice, and process parameters have to be estimated from different phase I data sets collected by different practitioners. This fact causes the between‐practitioners variability among the properties of the synthetic‐type charts designed for each practitioner. In fact, the shape of the run length distribution of the synthetic‐type charts changes with the mean shift size. As a good alternative, the median run length false(MRLfalse) metric is argued to evaluate the properties of different control charts. In this paper, the MRL is used as a measure of the synthetic trueX¯ chart's performance, and the conditional MRL properties of the synthetic trueX¯ chart with unknown process parameters are investigated. Both the average MRL ( AMRL) and the standard deviation of MRL ( SDMRL) are used together to investigate the chart's properties when the process parameters are unknown. If the available number of phase I samples is not large enough to reduce the variability of the in‐control MRL values to an acceptable level, a bootstrap‐type approach is suggested to adjust the control limits of the synthetic trueX¯ chart and to further prevent many unwanted lower in‐control MRL values.

show abstract

Optimal design of a synthetic chart for monitoring process dispersion with unknown in-control variance

Cited by 14 publications

References 45 publications

The variable sampling interval run sumX‾control chart

The variable sampling interval run sumX‾control chart

An overview of synthetic‐type control charts: Techniques and methodology

Conditional median run length performance of the synthetic X¯ chart with unknown process parameters

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