On control chart for monitoring exponentially distributed quality characteristic

Adeoti, Olatunde Adebayo

doi:10.1177/0142331219868595

Cited by 13 publications

(9 citation statements)

References 33 publications

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“…The EWMA control chart is a memory type structure because it utilizes both current and past information (Adeoti, 2020). The EWMA statistic for a statistic ( θ ) is defined as…”

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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“…The EWMA control chart is a memory type structure because it utilizes both current and past information (Adeoti, 2020). The EWMA statistic for a statistic ( θ ) is defined as…”

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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“…For an efficient chart AR L 1 values should be smaller than competitors at specific shift in the process parameters (cf. Mehmood et al (2017), Das (2009) and Adeoti (2020)).…”

Section: Introductionmentioning

confidence: 99%

Enhanced nonparametric control charts under simple and ranked set sampling schemes

Abbas¹,

Nazir

Abid

et al. 2020

Transactions of the Institute of Measurement and Control

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Investigation and removal of unnatural variation in the processes of manufacturing, production and services require application of statistical process control. Control charts are the most famous and commonly used statistical process control tools to trace changes in the manufacturing and nonmanufacturing processes parameter(s). The nonparametric control charts become necessary when the distribution of underlying process is unknown or questionable. The nonparametric charts are robust alternative along with holding property of quick shift detection ability in process parameter(s). In this article, we have proposed nonparametric double exponentially weighted moving average chart based on Wilcoxon signed rank test under simple and ranked set sampling schemes for efficient monitoring of the process location. The proposed control charts are compared with classical exponentially weighted moving average, double exponentially weighted moving average, nonparametric exponentially weighted moving average sign, nonparametric exponentially weighted moving average signed rank, nonparametric cumulative sum signed rank charts using average run length and some other characteristics of run length distribution as performance measures. Comparison reveals that the proposed control charts performs better to detect all kinds of shifts in the process location than existing counterparts. A real-life application related to manufacturing process (the variable of interest is the diameter of piston ring) is also provided for the practical implementation of the proposed chart.

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“…These enhanced schemes include the double CUSUM (DCUSUM), hybrid EWMA (HEWMA), double EWMA (DEWMA), synthetic CUSUM, synthetic EWMA, etc. For more details on the enhancement of memory-type schemes, readers are referred to Abbas et al (2013), Adeoti (2020), Ali and Haq (2017), Capizzi and Masarotto (2010), Haq et al (2013), Malela-Majika (2020), Waldmann (1996), just to cite a few. For other alternative approaches of control charts, such as the use of divergence functions (e.g.…”

Section: Introductionmentioning

confidence: 99%

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

Thanwane

Malela‐Majika

Castagliola

et al. 2020

Transactions of the Institute of Measurement and Control

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Monitoring schemes are typically designed under the assumption of perfect measurements. However, in real-life applications, data tend to be subjected to measurement errors, that is, a difference between the real quantities and the measured ones mostly exist even with highly sophisticated advanced measuring instruments. Thus, in this paper, the negative effect of measurement errors on the performance of the homogenously weighted moving average (HWMA) scheme is studied using the linear covariate error model for constant and linearly increasing variance. Monte Carlo simulations are used to evaluate the performance of the proposed HWMA scheme in terms of the run-length characteristics. It is observed that as the smoothing parameter increases, measurement errors have a higher negative effect on the performance of the HWMA [Formula: see text] scheme. More importantly, it is shown that the negative effect of measurement errors is reduced by using multiple measurements and/or by increasing the slope coefficient of the covariate error model. Moreover, the performance of the HWMA [Formula: see text] scheme is compared with the corresponding exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) [Formula: see text] schemes. An illustrative example is provided to help in implementing this monitoring scheme in a real-life situation.

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On control chart for monitoring exponentially distributed quality characteristic

Cited by 13 publications

References 33 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

Enhanced nonparametric control charts under simple and ranked set sampling schemes

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

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