Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart

Yeong, Wai Chung; Khoo, Michael B. C.; Tham, L. K.; Teoh, Wei Lin; Rahim, M. A.

doi:10.1080/00224065.2017.11918004

Cited by 57 publications

(45 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Saccucci et al insisted that using two sampling intervals is optimal for detecting a specified shift of the process target value or any control scheme that can be represented as a Markov chain. A fixed couple ( h S , h L ) is then typically used, which is chosen from a suggested list as in the studies of Yeong et al and Castagliola et al Because h S represents the shortest feasible time interval between subgroups from the process, it is quite reasonable to fix the value of h S according to the manufacturing conditions. However, it seems not practical to fix the value of h L .…”

Section: Design Of Optimal Vsi Ewma‐rz Control Chartsmentioning

confidence: 99%

Monitoring the ratio of two normal variables using variable sampling interval exponentially weighted moving average control charts

Nguyen

Tran

Heuchenne

2018

Quality & Reliability Eng

View full text Add to dashboard Cite

We investigate in this paper a new type of control chart called VSI EWMA‐RZ by integrating the variable sampling interval feature (VSI) with the exponentially weighted moving average (EWMA) scheme to monitor the ratio of two normal random variables. Because the distribution of the ratio is skewed, we suggest designing two separated one‐sided charts instead of one two‐sided chart. A new coefficient is introduced allowing us to be free to choose a sampling interval provided that it optimizes the performance of the control chart. We also make a direct comparison between the VSI EWMA‐RZ charts and standard EWMA‐RZ control charts. The numerical simulations show that the proposed charts outperform the standard EWMA charts in detecting process shifts. An application is illustrated for the implementation of the VSI EWMA‐RZ control charts in the food industry.

show abstract

Section: Design Of Optimal Vsi Ewma‐rz Control Chartsmentioning

confidence: 99%

Monitoring the ratio of two normal variables using variable sampling interval exponentially weighted moving average control charts

Nguyen

Tran

Heuchenne

2018

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The standard CV chart is only efficient in the detection of large process shifts. Hence, numerous research works on univariate CV charts have been made over the years to improve the sensitivity of the standard CV chart in detecting small and moderate CV shifts (see, for example, Castagliola et al, Calzada and Scariano, Teoh et al, Yeong et al, Tran and Tran, Muhammad et al, to name a few). In a contrary, research works on monitoring the MCV arise.…”

Section: Introductionmentioning

confidence: 99%

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Chew

Khoo

Khaw

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.

show abstract

“…In this study, we suggest a VSI Shewhart control chart to monitor the multivariate CV. In this type of a strategy, the sampling interval is allowed to vary from sample to sample depending on the value of observations . If the multivariate CV value of a sample plotted in the chart tends to be out of control, the next sample should be collected in a shorter time to quickly detect the probable causes.…”

Section: Introductionmentioning

confidence: 99%

Variable sampling interval Shewhart control charts for monitoring the multivariate coefficient of variation

Nguyen¹,

Tran

Heuchenne

et al. 2019

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

In many industrial manufacturing processes, the ratio of the variance to the mean of a quantity of interest is an important characteristic to ensure the quality of the processes. This ratio is called the coefficient of variation (CV). A lot of control charts have been designed for monitoring the CV of univariate quantity in the literature. However, the CV control charts for multivariate quantity have not received much attention yet. In this paper, we investigate a variable sampling interval (VSI) Shewhart control chart for monitoring multivariate CV. The time between two consecutive samples is allowed to vary according to the previous value of the multivariate CV, which will help the chart to detect the process shifts faster. The comparison with the fixed sampling interval Shewhart chart is implemented to highlight the advantage of the VSI method. Finally, an illustrative example is demonstrated on real data.

show abstract

Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart

Cited by 57 publications

References 37 publications

Monitoring the ratio of two normal variables using variable sampling interval exponentially weighted moving average control charts

Monitoring the ratio of two normal variables using variable sampling interval exponentially weighted moving average control charts

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Variable sampling interval Shewhart control charts for monitoring the multivariate coefficient of variation

Contact Info

Product

Resources

About