Design issues for adaptive control charts

Jensen, Willis A.; Bryce, G. Rex; Reynolds, Marion R.

doi:10.1002/qre.911

Cited by 57 publications

(49 citation statements)

References 35 publications

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“…It is worth to note that this Markov chain approach has also been used by a variety of authors in establishing control chart properties, see Costa (1994Costa ( , 1997Costa ( , 1999 and Jensen, Bryce, and Reynolds (2008).…”

Section: Implementation Of the R R Rz Control Chartmentioning

confidence: 99%

Monitoring the ratio of two normal variables using Run Rules type control charts

Tran

Castagliola

Celano

2015

International Journal of Production Research

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Recent studies show that Shewhart-type control charts monitoring the ratio of two normal random variables are useful to perform continuous surveillance in several manufacturing environments; anyway, they have a poor statistical sensitivity in the detection of small or moderate process shifts. The statistical sensitivity of a Shewhart control chart can be improved by implementing supplementary Run Rules. In this paper, we investigate the performance of Phase II Run Rules Shewhart control charts monitoring the ratio with each subgroup consisting of n > 1 sample units. A Markov chain methodology coupled with an efficient normal approximation of the ratio distribution is used to evaluate the statistical performance of these charts. We provide an extensive numerical analysis consisting of several tables and figures to discuss the statistical performance of the investigated charts for deterministic and random shift sizes affecting the in-control ratio. An illustrative example from the food industry is provided for illustration.

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Section: Implementation Of the R R Rz Control Chartmentioning

confidence: 99%

Monitoring the ratio of two normal variables using Run Rules type control charts

Tran

Castagliola

Celano

2015

International Journal of Production Research

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“…Morevoer, Jensen et al used estimated parameters for adaptive charts. Costa and De Magalhaes utilized the concept of non‐central Chi‐square statistic for simultaneous monitoring in single adaptive control chart to enhance the robustness of the adaptive control chart for non‐normal data.…”

Section: Introductionmentioning

confidence: 99%

An adaptive EWMA scheme‐based CUSUM accumulation error for efficient monitoring of process location

Zaman

Lee

Riaz

et al. 2017

Quality & Reliability Eng

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The examination of product characteristics using a statistical tool is an important step in a manufacturing environment to ensure product quality. Several methods are employed for maintaining product quality assurance. Quality control charts, which utilize statistical methods, are normally used to detect special causes. Shewhart control charts are popular; their only limitation is that they are effective in handling only large shifts. For handling small shifts, the cumulative sum (CUSUM) and the exponential weighted moving average (EWMA) are more practical. For handling both small and large shifts, adaptive control charts are used. In this study, we proposed a new adaptive EWMA scheme. This scheme is based on CUSUM accumulation error for detection of wide range of shifts in the process location. The CUSUM features in the proposed scheme help with identification of prior shifts. The proposed scheme uses Huber and Tukey bisquare functions for an efficient shift detection. We have used average run length (ARL) as performance indicator for comparison, and our proposed scheme outperformed some of the existing schemes. An example that uses real‐life data is also provided to demonstrate the implementation of the proposed scheme.

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“…They also have applied the notion of adaptive design parameters to other control charts. See, for example, Amin and Widmaier, Costa, Aparasi and Haro, Costa and Rahim, Epprecht et al ., Zimmer et al ., Reynolds and Stoumbos, Wu et al ., Yu and Hou, Celano et al ., Chen, Wu et al ., Yang and Su, Mahadik and Shirke, Jiang et al ., Jensen et al ., Zarandi et al ., and references therein. Some recent references include Luo et al ., Wu et al ., Shi et al ., De Magalhães et al ., Celano, Faraz and Moghadam, Mahadik and Shirke, Li and Wang, Dai et al ., and Epprecht et al …”

Section: Introductionmentioning

confidence: 99%

Variable Sample Size and Sampling Interval Charts with Runs Rules for Switching Between Sample Sizes and Sampling Interval Lengths

Mahadik¹

2012

Quality & Reliability Eng

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Variable sample size and sampling interval (VSSI) trueX¯ charts are substantially more efficient than are the static trueX¯ charts. However, the frequent switches between sample sizes and sampling interval lengths can be a complicating factor during the implementation of these charts. In this article, runs rules are proposed for switching between the sample sizes and the sampling interval lengths of these charts to reduce the frequency of switches. The expressions for the performance measures for the charts with these runs rules are developed. The methods presented are general and can be applied to other VSSI Shewhart control charts. The effects of different runs rules on the performances of the charts are compared numerically. The runs rules substantially reduce the frequency of switches. Some runs rules do not significantly alter the statistical performances of the charts; however, some adversely affect that in detecting large shifts in the process mean. Copyright © 2012 John Wiley & Sons, Ltd.

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Design issues for adaptive control charts

Cited by 57 publications

References 35 publications

Monitoring the ratio of two normal variables using Run Rules type control charts

Monitoring the ratio of two normal variables using Run Rules type control charts

An adaptive EWMA scheme‐based CUSUM accumulation error for efficient monitoring of process location

Variable Sample Size and Sampling Interval Charts with Runs Rules for Switching Between Sample Sizes and Sampling Interval Lengths

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