臺灣博碩士論文加值系統

過去品質管理之製程管制中，傳統統計製程管制(statistic process control, SPC)工具大都具有ㄧ假設前提，假設生產線上各產品觀測值間互為統計獨立，一旦製程為符合該前提下，管制能力皆能達到令人滿意之監控效果；然今時不同往日，生產流程之轉變，使得現今實務上，製程多屬相關性製程，製程資料間存在自我相關之交互作用，如此限制了傳統SPC方法於應用上之有效性。
有鑒於傳統SPC無法有效監控於相關性製程，本研究提出以新興人工智慧工具─支援向量機(support vector machine, SVM)，做為改善監控相關性製程之管制工具，並以實務上常出現之一階自我迴歸時間序列模式(AR(1))及階梯式干擾，模擬相關性製程之資料為研究對象。
本研究建構兩種製程監控模式，一為判別製程正常與否之二類模式，另一為辨識製程平均數偏移量之四類模式，分別以平均連串長度（average run length, ARL）及辨識正確率做為指標以驗證所提方法之有效性，並與傳統工具進行比較。
研究結果顯示，在二類模式中，所提方法於各情況下，皆能對相關性製程資料進行有效監控，尤其於製程異常存在平均數位移情況時，能較傳統X chart得到更令人滿意之結果；四類模式中，本研究所提之SVM監控模式，於80%之情形下，本模式優於傳統時間序列管制圖之表現，且顯著提升其監控正確率，唯於自我相關係數不大於0( )且平均數不具位移(0 )情況下，雖正確率明顯低於時間序列管制圖，然亦具至少80%以上之辨識正確率。
根據研究成果，說明了支援向量機應用於監控相關性製程之可行性，及其發展潛力。

Traditional statistical process control (SPC) techniques are not applicable in many process industries due to the assumption of uncorrelated datasets. Several methods have been proposed to deal with autocorrelated parameters. In this research, we demonstrate that support vector machine (SVM) can be effective tools to identify shifts in process parameter values from AR(1) processes with various values of the autocorrelation coefficient.
There are two kinds of monitoring models built in this thesis. One is 2 classifications model identifying if there is any shift existing for generated process data; the other one is 4 classifications model recognizing the quantity of mean shift of the data sets which contain one, two and three standard deviations shifted from non-shifted data and non-shifted data. Theses two models were estimated by average run length (ARL) and correct classification percentages respectively.
As the results reveal, SVM performed superiorly than X chart in all conditions in 2 classes experiment; in 4 classes, the proposed SVM model effected greater than time serious control chart in 80% conditions. For , SVM can be above 80% accurate with the 0 shift where time serious control chart is more effective than SVM. Therefore, the results show the possibility that SVM can be applied to improve control in manufacturing processes that generate correlated process data.

目 錄

第 壹 章　緒論 1
第 一 節　研究背景 1
第 二 節　研究動機 2
第 三 節　研究目的 3
第 四 節　研究範圍 4
第 五 節　本文架構 5
第 貳 章　文獻探討 7
第 一 節　自我相關之統計製程管制法 7
第 二 節　人工智慧於統計製程管制 8
第 三 節　支援向量機 10
第 參 章　研究方法 11
第 一 節　自我相關時間序列 11
第 二 節　支援向量機 12
第 肆 章 SVM製程監控模式 21
第 一 節 研究架構 21
第 二 節　監控模式之訓練 23
第 伍 章 實驗結果分析 31
第 一 節 二類模式 31
第 二 節　四類模式 45
第 陸 章 結論與建議 53
第 一 節 結論 53
第 二 節　研究貢獻 54
第 三 節　後續研究建議 54
參考文獻 57
附錄一 SVM監控模式於二類模式中不同參數組合下之ARL數據 63
附錄二 二類模式使用尺度化資料之結果 69


表 目 錄

表 3-2-1　SVM常用的核心函數 19
表 4-2-1　訓練資料子集A 27
表 4-2-2 訓練資料子集B 28
表 5-1-1　SVM監控模式結果A、結果B及X chart管制圖之ARL比較表 33
表 5-1-2 訓練資料子集C 37
表 5-1-3　SVM監控模式結果C與結果B之ARL比較表 38
表 5-1-4　訓練資料子集D 39
表 5-1-5　SVM監控模式結果D與結果C之ARL比較表 40
表 5-1-6　SVM監控模式參數變化之ARL比較表(gamma固定為2-5) 44
表 5-2-1 SVM監控模式及時間序列管制圖之辨識正確率(%)比較表 46
表 5-2-2 資料尺度化後SVM及時間序列管制圖之辨識正確率(%)比較表 49
表 5-2-3　第一次修正訓練資料組合(scaled)所得SVM監控模式及時間序列管制圖之辨識正確率(%)比較表 50
表 5-2-4　第二次修正訓練資料組合(scaled)所得SVM監控模式及時間序列管制圖之辨識正確率(%)比較表 52


圖 目 錄

圖 1-5-1　本文架構示意圖 6
圖 3-2-1　SVM基本概念圖 14
圖 3-2-2　最佳分割超平面示意圖 14
圖 3-2-3　最適分類標準平面 15
圖 3-2-4　線性不可分之SVM概念圖 17
圖 3-2-5　核心函數轉換示意圖例 19
圖 4-1-1　研究架構圖 22
圖 4-2-1　本研究所用AR(1)資料分布圖 24
圖 4-2-2　同一刻度下本研究所用AR(1)資料分布圖 25
圖 4-2-3　SVM監控模式之網路架構 26
圖 5-1-1　結果A與X chart之ARL比較 34
圖 5-1-2　結果B與X chart之ARL比較 35
圖 5-1-3　結果A與結果B之ARL比較 36
圖 5-1-4　結果C與結果B於製程平均數不具位移(shift=0 )時之ARL比較表 38
圖 5-1-5　變化參數C值比較 42
圖 5-1-6　變化參數gamma值之比較 43
圖 5-1-7　較佳參數組合之比較 44
圖 5-2-1　正自我相關係數平均數具位移資料分布圖 47
圖 5-2-2　尺度化後AR(1)資料分布圖 48

中文部分
1.陳學群(2006)。應用獨立成份分析、支援向量迴歸及類神經網路於財務時間序列預測模式之建構。輔仁大學應用統計研究所碩士論文，台北縣。

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