Synthetic control charts with two-stage sampling for monitoring bivariate processes

Costa, Antônio Fernando Branco; Machado, Marcela Aparecida Guerreiro

doi:10.1590/s0101-74382007000100007

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Their numerical comparisons showed that the autocorrelation has a negative effect on the side‐sensitive schemes and they are inferior to the usual S1 T 2 chart, especially for variables with a high correlation coefficient. Costa and Machado considered a correlated bivariate process using a two‐stage sampling procedure with a S1 T 2 chart. More recently, Dargopatil and Ghute proposed a multivariate analogue of Hu and Sun using skipping and mixed sampling strategies to reduce the negative effect of autocorrelation.…”

Section: Multivariate Synthetic Chartsmentioning

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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Section: Multivariate Synthetic Chartsmentioning

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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“…Control charts are statistical procedures that have been generally utilized in checking item quality during the production process. More insights regarding the usual control chart can be found in Costa and Machado (2007) and Claro et al (2008). Schilling and Neubauer (2017) suggested that the reliability sampling plans are utilized to decide the adequacy of the item at some future point in its operational life.…”

Section: Introductionmentioning

confidence: 99%

Repetitive Acceptance Sampling Plan for Lifetimes Following a Skew-Generalized Inverse Weibull Distribution

Singh¹,

Singh

Kanwar³

et al. 2022

Pesqui. Oper.

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In this paper, we consider a skew-generalized inverse Weibull probability distribution for repetitive acceptance sampling plans based on truncated life tests with known shape parameter. The design parameters such as sample size and acceptance numbers are evaluated by considering the median life time of the test units as a quality parameter under the constraint of two risks, known as the producer's risk and consumer's risk at a certain level. We explained the proposed method with the help of tables for different values of the known parameter. The skew-generalized inverse Weibull distribution fits better a real data set considered than the generalized inverse Weibull distribution. Comparison between the proposed plan and the single sampling plan is presented.

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“…Many innovations have been proposed to improve the performance of the T 2 charts. Costa and Machado (2007) studied the properties of the synthetic T 2 chart with two-stage sampling. Machado and Costa (2008b) considered the use of simultaneous X charts as an alternative to the use of the T 2 chart.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the mean vector and the covariance matrix of multivariate processes with sample means and sample ranges

Costa¹,

Machado²

2011

Prod.

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The joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and R charts and the joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and S² charts are the most common charts used for monitoring the process mean and dispersion. With the usual sample sizes of 4 and 5, the joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and R charts are slightly inferior to the joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and S² charts in terms of efficiency in detecting process shifts. In this article, we show that for the multivariate case, the charts based on the standardized sample means and sample ranges (MRMAX chart) or on the standardized sample means and sample variances (MVMAX chart) are similar in terms of efficiency in detecting shifts in the mean vector and/or in the covariance matrix. User's familiarity with the computation of sample ranges is a point in favor of the MRMAX chart. An example is presented to illustrate the application of the proposed chart.

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Synthetic control charts with two-stage sampling for monitoring bivariate processes

Cited by 12 publications

References 32 publications

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

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

Repetitive Acceptance Sampling Plan for Lifetimes Following a Skew-Generalized Inverse Weibull Distribution

Monitoring the mean vector and the covariance matrix of multivariate processes with sample means and sample ranges

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