在這篇論文中，發表了三種新型管制圖用於監控完全資料和型II設限資料的韋伯低百分位。通過把韋伯分佈轉換成最小極值分佈，Pascaul et al. (2017)發表了一種新型的EWMA管制圖，稱之為基於條件與輔助統計量的樞紐量的EWMA-SEV-Q管製圖。延伸這個想法來創建一種CUSUM管製圖，稱之為CUSUM-SEV-Q。也提供了關於監控統計量更深入的探討。此外，通過把韋伯分佈轉換為標準常態分佈，發表了EWMA和CUSUM管製圖，分別稱之為EWMA-YP和CUSUM-YP，用來監控完全資料的韋伯百分位。在完全資料，通過比較ARL，EWMA-YP和CUSUM-YP管製圖表現優於EWMA-SEV-Q和CUSUM-SEV-Q管製圖。在設限資料，EWMA-SEV-Q表現優於CUSUM-SEV-Q。兩個數值案例用來描繪所發表管制圖的實際應用。

In this paper, we propose three new control charts for monitoring the lower Weibull percentiles under complete data and Type-II censoring. In transforming the Weibull distribution to the smallest extreme value distribution, Pascaul et al. (2017) presented an exponentially weighted moving average (EWMA) control chart, hereafter referred to as EWMA-SEV-Q, based on a pivotal quantity conditioned on ancillary statistics. We extended their concept to construct a cumulative sum (CUSUM) control chart denoted by CUSUM-SEV-Q. We provide more insights of the statistical properties of the monitoring statistic. Additionally, in transforming a Weibull distribution to a standard normal distribution, we propose EWMA and CUSUM control charts, denoted as EWMA-YP and CUSUM-YP, respectively, based on a pivotal quantity for monitoring the Weibull percentiles with complete data. With complete data, the EWMA-YP and CUSUM-YP control charts perform better than the EWMA-SEV-Q and CUSUM-SEV-Q control charts in terms of average run length (ARL). In Type-II censoring, the EWMA-SEV-Q chart is slightly better than the CUSUM-SEV-Q chart in terms of ARL. Two numerical examples are used to illustrate the applications of the proposed control charts.

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