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首页> 外文期刊>International journal of productivity and quality management >A modified range (R) chart to monitor process dispersion of autocorrelated data
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A modified range (R) chart to monitor process dispersion of autocorrelated data

机译：修改后的范围（R）图表可监视自相关数据的过程分散

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摘要

The range (R) charts are widely used in industries to monitor the process dispersion. Monitoring process dispersion is as important as monitoring the process mean. In actual practice, some process outputs are correlated, the performance of R chart may have adverse effect on it. The performance of the chart is measured in terms of the average run length (ARL), which is the average number of samples before an out-of-control signal is obtained. Ultimately, the performance of these charts may be suspected due to autocorrelation. In this paper, an attempt is made to counter the autocorrelation by designing the new R chart named modified R chart, based on sum of chi-squares. The performance of this modified R chart is computed for sample sizes of 3 and 5. It is observed that when the level of correlation (Φ) increases, the performance of the modified R chart deteriorates. Moreover, the modified R chart for sample size of three and five is compared with adaptive R charts, suggested by Lee (2011) at zero level of correlation (Φ). It is found that modified R chart performs better than adaptive R charts for most of the cases.
机译：范围（R）图在工业中广泛用于监视过程分散。监视过程分散与监视过程平均值一样重要。在实际操作中，某些过程输出是相关的，R图表的性能可能会对它产生不利影响。图表的性能是根据平均运行长度（ARL）来衡量的，该平均运行长度是获得失控信号之前的平均样本数。最终，由于自相关，可能会怀疑这些图表的性能。在本文中，尝试通过基于卡方和设计新的R图（称为修改的R图）来抵消自相关。对于3和5的样本量，计算了此修改后的R图的性能。可以观察到，当相关程度（Φ）增大时，修改后的R图的性能会下降。此外，将修改后的R图表（样本大小为3和5）与自适应R图表进行比较，后者由Lee（2011）建议在零相关水平（Φ）进行。发现在大多数情况下，修改后的R图的性能优于自适应R图。

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