Attribute control chart for some popular distributions

Shafqat, Ambreen; Hussain, Jaffer; Al‐Nasser, Amjad D.; Aslam, Muhammad

doi:10.1080/03610926.2017.1335414

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…Additionally, it is evident that the Beta and Unit gamma distributions presents the same fit to the current data. In Figure 3, we present the control chart for the complete set of the available data (Phase I: 1-20; Phase II: [21][22][23][24][25][26][27][28][29][30][31][32][33][34] using error type I = 0.0027 to yield ARL 0 = 370. The control chart in Figure 3 does not trigger an alarm during Phase I, so we obtain no contradiction against the models.…”

Section: Application To a Real Data Setmentioning

confidence: 99%

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Control charts to monitor rates and proportions

Fernandes

Bourguignon

2018

Quality & Reliability Eng

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In this paper, we call attention for monitoring proportions and rates when they are not results of Bernoulli experiments. To deal with this problem, usually control charts based on Beta distributions are built as this distribution is a well‐known one. However, in practice, there are other distributions to be considered as Simplex and Unit Gamma distributions. The impact of the speed to signal a shift in the average proportion in terms of out‐of‐control average run length is measured when the control limits determined under a Beta distribution is equivocally used to monitor individual rates from Simplex or Unit Gamma distributions or vice versa.

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Section: Application To a Real Data Setmentioning

confidence: 99%

“…Other authors have combined attribute (as results of gauge classification) and variable charts to improve the performance in signaling out‐of‐control state. Attribute charts have also been used to monitor parameters of Weibull and exponential distributions.…”

Section: Introductionmentioning

confidence: 99%

Control charts to monitor rates and proportions

Fernandes

Bourguignon

2018

Quality & Reliability Eng

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“…Sanusi et al [9] introduced a different approach for the EWMA-based chart when supplementary information about the main variable is not constant. Some different applications of attributes and variables control charts can be found in [10,11].…”

Section: Introductionmentioning

confidence: 99%

A new CUSUM control chart under uncertainty with applications in petroleum and meteorology

et al. 2021

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In these last few decades, control charts have received a growing interest because of the important role they play by improving the quality of the products and services in industrial and non-industrial environments. Most of the existing control charts are based on the assumption of certainty and accuracy. However, in real-life applications, such as weather forecasting and stock prices, operators are not always certain about the accuracy of an observed data. To efficiently monitor such processes, this paper proposes a new cumulative sum (CUSUM) X¯ chart under the assumption of uncertainty using the neutrosophic statistic (NS). The performance of the new chart is investigated in terms of the neutrosophic run length properties using the Monte Carlo simulations approach. The efficiency of the proposed neutrosophic CUSUM (NCUSUM) X¯ chart is also compared to the one of the classical CUSUM X¯ chart. It is observed that the NCUSUM X¯ chart has very interesting properties compared to the classical CUSUM X¯ chart. The application and implementation of the NCUSUM X¯ chart are provided using simulated, petroleum and meteorological data.

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“…Several researchers have worked on different attribute control charts. More details about the attribute control charts can be found in Epprecht et al, 2 Wu and Jiao, 3 Joekes and Barbosa, 4 Aslam et al, 5 Rao, 6 Shafqat et al, 7 Arif et al. (2018), Rao et al, 8 and Adeoti and Ogundipe 9 and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Moving average control charts for the rayleigh and inverse rayleigh distributions under time truncated life test

Adeoti

Rao

2021

Quality & Reliability Eng

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In this paper, the moving average control chart is developed under time truncated test to monitor the number of failures when the lifetime of an item follows the Rayleigh and inverse Rayleigh distributions. The number of failures is detected when a planned number of items has been placed on a time truncated testing experiment. The average run lengths (ARL) for various parameter settings are used to evaluate the performance of the proposed control chart. Also, the control chart parameters such as test time multiplier (a) and chart coefficient (k) are obtained by contemplating the in‐control ARL for various sample sizes. The study revealed that the proposed control chart outperform the existing time truncated attribute control chart in the quick detection of shifts in process parameters. The application of the proposed chart methodology is also demonstrated using simulated data.

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Attribute control chart for some popular distributions

Cited by 17 publications

References 14 publications

Control charts to monitor rates and proportions

Control charts to monitor rates and proportions

A new CUSUM control chart under uncertainty with applications in petroleum and meteorology

Moving average control charts for the rayleigh and inverse rayleigh distributions under time truncated life test

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