統計製程管制(Statistical Process Control，SPC)常被用於做為製程改善的工具，其透過持續監控製程之手法，若製程發生變異，管制圖會在最短時間內發出警訊，使用者須採取必要行動，找出變異並使製程儘快回到穩定狀態。常見管制圖包含蕭華特、累積和以及指數加權移動平均管制圖等，而一般概似比(Generalized Likelihood Ratio，GLR)管制圖也在近年開始受到學者們的關注。
Reynolds and Lou (2010)提出一般概似比管制圖在常態分配時，使用者可選擇作者提供之管制界限達到特定的管制圖績效表現，且作者證明其能有效監控製程廣泛的偏移。過往一般概似比管制圖研究中大多假設製程參數已知且服從常態分配，即使製程不為常態，只要增加樣本數最終製程也會趨近於常態分配，但在現實中增加樣本數同時也增加使用者的成本，若在小樣本抽樣且製程與常態假設不符的情況時，將會使得管制圖發出警訊的頻率與預期的不符，增加在後續使用上的困難度。指數加權移動平均(Exponentially Weighted Moving Average，EWMA)管制圖被認為有著對非常態資料的穩健性(Non-Normality Robustness)，其被認定可有效改善管制圖在製程不為常態分配的績效表現，本研究將應用指數加權移動平均中的加權方法，透過對管制圖統計量做加權的方式，改善管制圖在製程資料與常態不符的情形，並以平均監控時間(Average Time to Signal，ATS)為績效指標，和過去學者所提出之指數加權移動平均以及累積和管制圖比較在製程資料改變時管制圖的績效表現。

Normal distribution has been widely assumed while using control chart. GLR (Generalized Likelihood Ratio, GLR) control chart is considered that can be very sensitive to different shift occurred in process. Prior researches have showed that the underlying data disobey the normal assumption that partitioners assumed, the performance of control chart would deteriorate.
EWMA (Exponentially Weighted Moving Average, EWMA) control chart can be robust to non-normal by weighting statistics of samples. Owing to the deteriorated effect caused by non-normal data, statistics of GLR control chart will be weighted to achieve robust to improve the change of underlying data. By doing so, GLR control chart can perform more effective than EWMA and CUSUM control chart when normal assumption is disobeyed.

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