On designing a sequential based EWMA structure for efficient process monitoring

Khaliq, Qurat-Ul-Ain; Omar, M. Hafidz; Riaz, Muhammad

doi:10.1080/16583655.2020.1712011

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…Hence, the term ( 1 − ( 1 − λ ) 2 normali ) tends to 1 if the sample tends to ∞. Some modifications on EWMA charts may be found in Riaz et al (2019), Osei-Aning and Abbasi (2020) and Abbasi et al (2020).…”

Section: Structure Of the Proposed Memory Type Control Chartsmentioning

confidence: 99%

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

Amin

Mahmood

Kinat

2020

Transactions of the Institute of Measurement and Control

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Control charts are commonly applied for monitoring and controlling the performance of the manufacturing process. Usually, control charts are designed based on the main quality characteristics variable. However, there exist numerous other variables which are highly associated with the main variable. Therefore, generalized linear model (GLM)-based control charts were used, which are capable of maintaining the relationship between variables and of monitoring an abrupt change in the process mean. This study is an effort to develop the Phase II GLM-based memory type control charts using the deviance residuals (DR) and Pearson residuals (PR) of inverse Gaussian (IG) regression model. For evaluation, a simulation study is designed, and the performance of the proposed control charts is compared with the counterpart memory less control charts and data-based control charts (excluding the effect of covariate) in terms of the run length properties. Based on the simulation study, it is concluded that the exponential weighted moving average (EWMA) type control charts have better detection ability as compared with Shewhart and cumulative sum (CUSUM) type control charts under the small or/and moderate shift sizes. Moreover, it is shown that utilizing covariate may lead to useful conclusions. Finally, the proposed monitoring methods is implemented on the dataset related to the yarn manufacturing industry to highlight the importance of the proposed control chart.

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Section: Structure Of the Proposed Memory Type Control Chartsmentioning

confidence: 99%

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

Amin

Mahmood

Kinat

2020

Transactions of the Institute of Measurement and Control

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“…Sampling methods are essential part of many research studies on a population [1][2][3][4]. However, sometimes study of a population division will become of resear cher's interest.…”

Section: Introductionmentioning

confidence: 99%

A new estimator and approach for estimating the subpopulation parameters

Salehi

Seber

2021

Journal of Taibah University for Science

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Based on some theoretical results, we recommend a new algorithm for estimating the total and mean of a subpopulation variable for the case of a known subpopulation size, which is different from the algorithm recommended by most of sampling books. The latter usually recommend the multiplication of the subpopulation sample mean by the subpopulation size rather than the subpopulation total estimator for the unknown subpopulation size. We present a criterion to determine which estimator is more efficient. The criterion shows that the traditional total subpopulation estimator for unknown subpopulation size will be more efficient if the subpopulation mean is close to zero. Using an innovative procedure, we develop a new estimator, and we study its properties using real data. The new estimator is potentially an appropriate direct estimator in a composite estimator for small area estimation.

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“…In recent past, many scholars have studied multicomponent stress-strength models largely owing to the growing interest on this topic, see for instance, works of [20][21][22][23], Dey and Moala [24] and many others. Seadawy et al [25], Seadawy et al [26], Ahmad et al [27], Riaz et al [28], Abbasi et al [29] have also studied some complex structures.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and non-Bayesian reliability estimation of multicomponent stress–strength model for unit Weibull distribution

Alotaibi

Tripathi

Dey³

et al. 2020

Journal of Taibah University for Science

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Mazucheli et al. introduced a new transformed model referred as the unit-Weibull (UW) distribution with uni-and anti-unimodal, decreasing and increasing shaped density along with bathtub shaped, constant and non-decreasing hazard rates. Here we consider inference upon stress and strength reliability using different statistical procedures. Under the framework that stress-strength components follow UW distributions with a known shape, we first estimate multicomponent system reliability by using four different frequentist methods. Besides, asymptotic confidence intervals (CIs) and two bootstrap CIs are obtained. Inference results for the reliability are further derived from Bayesian context when shape parameter is known or unknown. Unbiased estimation has also been considered when common shape is known. Numerical comparisons are presented using Monte Carlo simulations and in consequence, an illustrative discussion is provided. Two numerical examples are also presented in support of this study. Significant conclusion: We have investigated inference upon a stress-strength parameter for UW distribution. The four frequentist methods of estimation along with Bayesian procedures have been used to estimate the system reliability with common parameter being known or unknown.

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On designing a sequential based EWMA structure for efficient process monitoring

Cited by 6 publications

References 37 publications

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

A new estimator and approach for estimating the subpopulation parameters

Bayesian and non-Bayesian reliability estimation of multicomponent stress–strength model for unit Weibull distribution

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