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題名 Monitoring Process Variance Using an ARL-unbiased EWMA-P Control Chart.
作者 楊素芬
Yang, Su-Fen;Arnold, Barry C.
貢獻者 統計系
日期 2016
上傳時間 25-Apr-2017 15:49:33 (UTC+8)
摘要 Control charts are effective tools for signal detection in manufacturing processes. As much of the data in industries come from processes having non-normal or unknown distributions, the commonly used Shewhart variable control charts cannot be appropriately used, because they depend heavily on the normality assumption. The average run length (ARL) is generally used to measure the detection performance of a process when using a control chart, but it is biased for the monitoring statistic with an asymmetric distribution. That is, the ARL-biased control chart leads to take longer to detect the shifts in parameter than to trigger a false alarm. To overcome this problem, we herein propose an ARL-unbiased exponentially weighted moving average proportion (EWMA-p) chart to monitor the process variance for process data with non-normal or unknown distributions. We further explore the procedure to determine the control limits and to investigate the out-of-control variance detection performance of the ARL-unbiased EWMA-p chart. With a numerical example involving non-normal service times from a bank branch in Taiwan, we illustrate the applications of the proposed ARL-unbiased EWMA-p chart and also compare the out-of-control detection performance of the ARL-unbiased EWMA-p chart, the arcsin transformed symmetric EWMA variance, and other existing variance charts. The proposed ARL-unbiased EWMA-p chart shows superior detection performance. Thus, we recommend the ARL-unbiased EWMA-p chart for process data with non-normal or unknown distributions.
關聯 Quality and Reliability Engineering International, Vol.32, No.3, pp.1227–1235
資料類型 article
DOI http://dx.doi.org/10.1002/qre.1829
dc.contributor 統計系
dc.creator (作者) 楊素芬zh_TW
dc.creator (作者) Yang, Su-Fen;Arnold, Barry C.
dc.date (日期) 2016
dc.date.accessioned 25-Apr-2017 15:49:33 (UTC+8)-
dc.date.available 25-Apr-2017 15:49:33 (UTC+8)-
dc.date.issued (上傳時間) 25-Apr-2017 15:49:33 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/109217-
dc.description.abstract (摘要) Control charts are effective tools for signal detection in manufacturing processes. As much of the data in industries come from processes having non-normal or unknown distributions, the commonly used Shewhart variable control charts cannot be appropriately used, because they depend heavily on the normality assumption. The average run length (ARL) is generally used to measure the detection performance of a process when using a control chart, but it is biased for the monitoring statistic with an asymmetric distribution. That is, the ARL-biased control chart leads to take longer to detect the shifts in parameter than to trigger a false alarm. To overcome this problem, we herein propose an ARL-unbiased exponentially weighted moving average proportion (EWMA-p) chart to monitor the process variance for process data with non-normal or unknown distributions. We further explore the procedure to determine the control limits and to investigate the out-of-control variance detection performance of the ARL-unbiased EWMA-p chart. With a numerical example involving non-normal service times from a bank branch in Taiwan, we illustrate the applications of the proposed ARL-unbiased EWMA-p chart and also compare the out-of-control detection performance of the ARL-unbiased EWMA-p chart, the arcsin transformed symmetric EWMA variance, and other existing variance charts. The proposed ARL-unbiased EWMA-p chart shows superior detection performance. Thus, we recommend the ARL-unbiased EWMA-p chart for process data with non-normal or unknown distributions.
dc.format.extent 98 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Quality and Reliability Engineering International, Vol.32, No.3, pp.1227–1235
dc.title (題名) Monitoring Process Variance Using an ARL-unbiased EWMA-P Control Chart.
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1002/qre.1829
dc.doi.uri (DOI) http://dx.doi.org/10.1002/qre.1829