Monitoring exponential data using two-sided control charts with runs rules

Rakitzis, Athanasios C.

doi:10.1080/00949655.2014.998219

Cited by 29 publications

(10 citation statements)

References 28 publications

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“…For instance, Tran et al 28 used a run rules control chart in monitoring the random variables' ratio with a normal approximation of the ratio distribution. Rakitzis 29 suggested the two‐sided run rules charts monitor the process with exponential data. The results showed unbiased performances and the charts able to identify changes in the mean of a process from an exponential distribution.…”

Section: Introductionmentioning

confidence: 99%

An improved Hotelling's T² chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

Chew

Khoo

Khaw

et al. 2020

Appl Stoch Models Bus & Ind

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Quality improvement has been receiving great attention in industries. In recent years, the finite horizon process is commonly encountered in industries due to flexible manufacturing production. Past research works on finite horizon process monitoring are still limited. Because of this, three run rules Hotelling's T2 charts are proposed to monitor a finite horizon process. The performance measures of the proposed charts are derived using the Markov‐chain approach. The proposed schemes can serve as a framework for quality engineers who wish to perform process monitoring easily and efficiently. Numerical comparisons between the proposed and existing Shewhart (SH) T2 charts have been made. The statistical performance measures were investigated in this article. The findings reveal that the proposed charts outperform the SH T2 chart for detecting small and moderate process shifts in a finite horizon process. The illustration of the run rules (RR) T2 chart is shown on a real manufacturing dataset in a finite horizon process.

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Section: Introductionmentioning

confidence: 99%

An improved Hotelling's T² chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

Chew

Khoo

Khaw

et al. 2020

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…Qu et al 10 studied several TBE charts under sampling inspection. Rakitzis 11 proposed a two‐sided control chart with run rules for monitoring exponential TBE. Yang et al 12 studied the design of gamma control charts based on the average time to signal (ATS).…”

Section: Introductionmentioning

confidence: 99%

Monitoring reliability for a gamma distribution with a double progressive mean control chart

Alevizakos

Koukouvinos

2020

Quality & Reliability Eng

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Control charting technique for time between events (TBE) is very important in high‐yield processes for monitoring reliability. For a regularly maintained system, the interfailure times can be modeled by a gamma distribution. This article proposes a new control chart based on the double progressive mean statistic for monitoring the time between k (≥1) failures of a maintained gamma distributed system (referred as DPM‐TBE chart). The performance of the proposed scheme is measured in terms of the average run‐length (ARL) for the case when the scale parameter is known as well as when it is unknown and is estimated from an in‐control (IC) reference sample. A comparison study with other TBE charts shows that the DPM‐TBE chart is more effective. In addition, the proposed chart is shown to be very robust for large shifts when the true distribution of time between failures is a Weibull or a lognormal. Finally, an illustrative example is given to demonstrate the implementation of the proposed chart.

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“…Zhang et al 9 proposed a Shewhart‐type TBE chart based on the gamma distribution using the random‐shift model. In addition, Santiago and Smith 10 presented a transformation approach to the construction of t chart and they also applied runs rules, while Rakitzis 11 studied an average run length (ARL)‐unbiased two‐sided Shewhart‐type TBE chart with runs rules. Huang and Yang 12 developed an ARL‐unbiased design of the gamma control chart with equal‐probability limits.…”

Section: Introductionmentioning

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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Monitoring exponential data using two-sided control charts with runs rules

Cited by 29 publications

References 28 publications

An improved Hotelling's T² chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

An improved Hotelling's T² chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

Monitoring reliability for a gamma distribution with a double progressive mean control chart

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

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Monitoring exponential data using two-sided control charts with runs rules

Cited by 29 publications

References 28 publications

An improved Hotelling's T2 chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

An improved Hotelling's T2 chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

Monitoring reliability for a gamma distribution with a double progressive mean control chart

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

Contact Info

Product

Resources

About

An improved Hotelling's T² chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach

An improved Hotelling's T² chart for monitoring a finite horizon process based on run rules schemes: A Markov‐chain approach