Abstract
Much research has been conducted on two-sided Exponentially Weighted Moving Average (EWMA) control charts, while less work has been devoted to the one-sided EWMA charts. Traditional one-sided EWMA charts involve resetting the EWMA statistic to the target whenever it falls below or above the target, or truncating the observations above or below the target and further applying the EWMA statistic to the truncated samples. In order to further improve the performance of traditional one-sided EWMA mean (X¯) charts, this paper studies the performance of the Modified One-sided EWMA (MOEWMA) X¯ charts to monitor a normally distributed process. The Monte-Carlo simulation method is used to obtain the zero- and steady-state Run Length (RL) properties of the proposed control charts. Through extensive simulations and comparisons with other charts, it is shown that the proposed MOEWMA X¯ charts compare favorably with some existing competing charts. Moreover, by attaching the variable sampling intervals (VSI) feature to the MOEWMA X¯ charts, it is shown that the VSI MOEWMA charts outperform the corresponding charts without the VSI feature. Finally, a real data example from manufacturing process shows the implementation of the proposed one-sided charts.
Highlights
Received: 15 November 2021A main objective for a process is to continuously improve its quality, which can be statistically expressed as variation reduction
We study the performance of one-sided Modified One-sided EWMA (MOEWMA) Xchart withoutand with variable sampling intervals (VSI) features
MOEWMA Xchart are investigated by using extensive Monte-Carlo simulations
Summary
A main objective for a process is to continuously improve its quality, which can be statistically expressed as variation reduction. For the detection of small to moderate shifts, both CUSUM and EWMA charts using the current and former samples information perform much better than Shewhart type charts, see Montgomery [9]. While their work studied the EWMA chart for monitoring the CV, as far as we know, there is no research on the proposed scheme for monitoring the mean of a normally distributed process. A normally distributed quality characteristic usually exists in some industrial processes To fill this gap, we investigate the properties of the MOEWMA Xcharts. Coelho et al [25] proposed a VSI nonparametric Shewhart type control chart, which was shown to be better than the existing FSI chart. Some conclusions and recommendations are made in the last section
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