臺灣博碩士論文加值系統

在統計製程管制 (Statistical Process Control, SPC) 中，當製程存在可歸屬原因時，其製程可能會表現出特定的非隨機性樣式的變化。正確辨認率非隨機性樣式，可以縮小診斷製程異常原因之範圍。若能估計參數之變化量，更有助於規劃改善方案。本研究之目的是發展一個辨認系統，可以有效地辨認非隨機性樣式之種類，並估計其參數。本研究將考慮單一樣式出現及多種樣式併發之情形。
在本研究中，我們提出兩種方法來做管制圖非隨機性樣式之辨認及重要參數之估計。第一種方法是以計算累積和平均為基礎之原創方法，若將累積和平均以圖形方式表示，不僅可協助使用者辨認非隨機性樣式，同時也可以由圖形估計樣式之重要參數。
第二種方法是以類神經網路為基之辨認程序。我們的主要貢獻有下列幾點。第一，我們發展啟發式特徵擷取方法。這些特徵不僅可提高應用時之辨認能力，同時也可降低訓練時間及應用時之計算時間。第二，我們以類神經網路發展一個篩選訓練樣本之程序，將不良之樣本篩選後，有助類神經網路之學習並提高類神經網路之一般化能力。
本研究所建立之辨認系統是以辨認率、平均連串長度及平均百分比誤差來評估其效益。經由模擬方法分析，證明本研究所提出之方法可以有效地辨認非隨機性樣式並估計其參數。

Control chart patterns recognition is an important aspect of statistical process control (SPC). A control chart may indicate an out-of-control condition when some nonrandom patterns occur. Different nonrandom patterns can be associated with a specific set of assignable causes. Hence, identification of nonrandom patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic search could be reduced in length. The purpose of this research is to develop a pattern recognition system to recognize nonrandom patterns and identify the key parameters.
In this research we propose two pattern recognizers for the analysis of control chart patterns: a graphical approach and a neural network-based pattern recognizer. The proposed graphical approach is based on the concepts of data smoothing and is capable of detecting unnatural patterns as well as describing the key parameters of the specific pattern detected.
The neural network-based pattern recognizer looks for the following nonrandom patterns: trend, cycle, shift and the multiple combinations of these patterns. In addition to the raw data, some important features extracted from process data were used as the inputs of neural network.
The pattern recognizers have been evaluated by estimating the average run length, the rate of correct classification and mean absolute percent errors. The simulation results show that the pattern recognizer can recognize the control chart patterns with correct classification rate of about 90%. The results also show that the pattern recognizers developed in this research can accurately identify the key parameters of the patterns detected.

封面
中文摘要
英文摘要
誌謝
目錄
表目錄
圖目錄
一、緒論
1.1 研究背景
1.2 研究目的
1.3 研究範圍
1.4 研究方法與步驟
1.5 論文架構
二、文獻探討
2.1 統計製程管制圖
2.2 管制圖非隨機性樣式之辨認
2.3 類神經網路於製程數據分析之應用
三、管制圖非隨機性樣式之辨認及參數估計之圖形方法
3.1 圖形法之原理
3.2 數學符號定義
3.3 非隨機性樣式之辨認及參數之估計
3.4 範例說明
3.5 圖形法於線上品管的應用
四、類神經網路
4.1 類神經網路概述
4.2 倒傳遞網路
4.3 FUZZY ART類神經網路
五、以類神經網路辨認管制圖之非隨機性樣式及其參數之估計
5.1 分析視窗
5.2 辨認系統之架構
5.3 訓練樣本的產生
5.4 訓練樣本之篩選
5.5 特徵與輸入向量
5.6 類神經網路之架構
5.7 實務應用上訓練樣本之取得
六、實驗結果及評估
6.1 測試方式
6.2 效益評估指標
6.3 實驗結果及分析
七、結論與展望
7.1 結論
7.2 未來展望
八、參考文獻

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