管制圖是用來確定製造過程是否在統計管制狀態下的一種工具，本研究則基於同時監控製程平均數與變異數的情況下，提出了一套用於管制圖警訊後的診斷工具，若於警訊後提供更多相關的診斷資訊，則可促進工程師對於該變異之識別。常見的警訊後相關診斷資訊有製程平均數之估計值、製程變異數之估計值、變異點之估計、參數偏移種類與偏移方向之辨識，而常見的管制圖有累積和管制圖和指數加權移動平均管制圖，雖然其可偵測出製程參數偏移的微小變化，並也可於警訊後，藉由其診斷對於參數偏移的方向、種類與規模等進行識別，但都需要針對特定位移做最佳化的參數設定，若設置的參數不符合實際偏移情況時，管制圖性能將受到影響，基於上述，本研究則使用基於Kullback-Leibler information所建立起的資訊管制圖，其可用於同時監控製程平均數與製程變異數，不需要參數設定，且可用於偵測大範圍之偏移；在於此管制圖警訊後，本研究則也對其提出一個警訊後之診斷方法。本研究所提出之診斷方法，為對於製程參數的偏移情形進行識別，而製程參數的偏移情形有三種，分別為(1)僅製程平均數偏移(2)僅製程變異數偏移(3)製程平均數與製程變異數皆偏移，不同的可歸屬原因之影響也導致了上述不同情形之發生，因此本研究則基於貝氏模型平均法，事先對於上述模型進行定義，並且在KLI管制圖警訊後，藉由貝氏模型平均法，可對於以上偏移情形進行相對機率之估計，估計出之相對機率則可供辨識當前製程參數之偏移情形。在製程參數偏移規模較小時，採用貝氏模型平均法於辨識當前參數偏移情境之準確度較低，但是當製程參數偏移規模擴大時，採用此法之辨識準確度也隨之提升，並且其在辨識僅製程平均數偏移與製程平均數與製程變異數皆偏移之情況下，會有較高的準確度。

The objective of the thesis is to provide a diagnostic tool while applying Kullback-Leibler information (KLI) control chart for monitoring process mean and process variance simultaneously, utilizing KLI control chart needs not parameter adjustment, it only requires finding the control limit. Once the KLI control chart signals, in this research, we are going to identify types of parameter shifts, which are,1) Only change in process mean,2) Only change in process variance,3) Both changes in process mean and variance, respectively, since the identification of above schemes can streamline the search for diagnosing the assignable causes. After the control chart signals, we then exert Bayesian model averaging (BMA) as the measure to identify the above schemes. In this study, the result shows that using BMA as steps has higher accuracy in identifying the schemes of only change in process mean and both changes in process mean and variance, the accuracy ascends when there is larger shift in process parameter as well, yet it descends in small shift.

中文文獻:
賴芳妤，Kullback-Leibler資訊管制圖應用於同時監控製程平均數及變異數，國立成功大學工業與資訊管理研究所碩士論文，民國一百一十一年六月。
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