SPC管制圖是最常使用在監測製程是否產生改變的工具，當失控信號產生時，表示製程已經受到干擾而產生改變，工程師必須判斷造成製程改變是否為可歸屬原因(assignable cause)，並進一步尋找製程真正的改變時間以排除製程失控狀態，然而製程真實發生改變後還需要一段時間才會在管制圖中產生失控信號，因此估計出製程真正的改變點(change point)能夠幫助工程師更快速地找到可歸屬原因並改善失控製程。
在實務製程中，工程師無法事先得知製程的改變類型，若將線性估計量(linear estimator)使用在製程改變類型為階梯式改變(step change)時，或是將階梯式估計量(step estimator)使用在製程改變類型為線性偏移(linear trend)時，就會產生估計錯誤導致工程師誤判，因此本研究使用斜率判定係數法(slope coefficient of determination approach)估計出製程判定斜率，並應用模糊統計集群法(fuzzy statistical clustering approach)於ｐ管制圖中來估計製程改變點，最後經由模擬結果及實例驗證發現，使用斜率判定係數法判定製程為階梯式改變或是線性偏移的分類並沒有達到很有效的分類正確率，未來研究方向可針對不同的參數設定值進行模擬以提高分類正確率。

Statistical process control (SPC) charts are the most popular tools used to monitor process shift. When it generates an out-of-control signal in a SPC chart, it means the disturbance is present in the process. Engineers have to identify the assignable cause and eliminate it. However, the out-of-control signal is usually followed by a considerable amount of delay from the actual time of the change. The method of identity the actual time of change is knows a change point estimation problem.
In practice, engineers cannot know in advance the type of change in process. The inaccurate estimation would result them to misdiagnose if using linear estimator in a step change process or if using step estimator in a linear trend process. In this work, the slope coefficient of determination approach was used to estimate the slope of the linear trend. According to the value of the estimated slope, the fuzzy statistical clustering approach was then used to identify the change point in p control chart. Finally, the simulation results and practice example are conducted to verify and validate whether the slope coefficient of determination approach enables effectively detecting step changes or linear trend changes in a process. The results show that the classification accuracies of the proposed method are not high enough. In future work, the researchers can discuss the proposed method with the different parameter settings.

摘要......................................i
ABSTRACT.................................ii
誌謝....................................iii
目錄.....................................iv
表目錄...................................vi
圖目錄..................................vii
第一章 緒論...............................1
1.1 研究背景..........................1
1.2 研究目的..........................4
1.3 研究流程..........................4
第二章　文獻探討...........................6
2.1　計數管制圖的改變點類型.................6
2.2　計數管制圖的類型......................8
2.3　二項分配之計數管制圖的改變點估計方法....13
2.4　模糊統計集群法........................15
2.4.1　隸屬函數(Membership Function)......16
2.4.2　目標函數(Objective Function).......16
2.5　本章總結.............................17
第三章　研究方法...........................18
3.1　研究假設.............................20
3.2　資料模擬.............................21
3.2.1　階梯式改變資料模擬..................21
3.2.2　線性偏移資料模擬....................24
3.3　p管制圖之管制界限.....................28
3.4　隸屬函數.............................29
3.4.1　階梯式與線性偏移下p管制圖之隸屬函數...29
3.5　p管制圖之目標函數.....................30
3.6　績效評比.............................31
3.7　斜率判定係數法........................32
3.8　建立斜率判定係數法準則.................33
3.8.1　斜率判定係數法模擬結果...............33
3.8.2　使用斜率判定係數準則之分類正確率......43
3.9　模糊統計集群法之流程概述...............52
第四章　模擬結果與實例驗證..................54
4.1　製程為階梯式改變......................54
4.2　製程為線性偏移........................59
4.3　實例驗證.............................64
第五章　結論與未來方向.....................67
5.1　結論.................................67
5.2　未來方向.............................68
參考文獻..................................69

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