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研究生:王怡仙
研究生(外文):Wang, Yi-Hsieh
論文名稱:應用機器學習方法以辨識SPC-EPC製程之管制圖型樣
論文名稱(外文):Application of Machine Learning Approaches to the Recognition of Control Chart Patterns for an SPC-EPC Process
指導教授:邵曰仁邵曰仁引用關係
指導教授(外文):Shao, Yueh-Jen
口試委員:侯家鼎呂奇傑
口試委員(外文):Hou, Chia-DingLu, Chi-Jie
口試日期:2015-06-28
學位類別:碩士
校院名稱:輔仁大學
系所名稱:統計資訊學系應用統計碩士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:129
中文關鍵詞:統計製程管制工程製程管制管制圖型樣機器學習製程干擾
外文關鍵詞:Statistical process controlEngineering process controlControl chart patternMachine LearningProcess disturbance
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  • 被引用被引用:2
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在工業製程中,統計製程管制(Statistical Process Control; SPC)和工程製程管制(Engineering Process Control; EPC)被廣泛地整合應用,以有效改善製程。雖然SPC及EPC的整合,對製程品質控管上有許多益處,但此整合系統卻可能導致管制圖型樣(Control Chart Pattern; CCP)難以辨識的問題:EPC的調整將造成製程干擾型樣被隱藏在管制圖內,因而將大幅增加CCP的辨識困難度。由於CCP通常會對應到失控原因的確認,若能準確地辨識CCP,對製程改善將有很大助益,也因此,能正確及快速辨認出不穩定製程中,製程干擾為何種的型樣,是一項很重要的研究議題。目前只有少數研究有探討到SPC-EPC整合製程下的CCP辨識議題;為了有效辨認出隱藏在SPC-EPC製程下的CCP,本文應用六種機器學習技術以辨識製程干擾,他們分別為:類神經網路(Artificial Neural Network)、支援向量機(Support Vector Machine)、極限學習機(Extreme Learning Machine)、時間延遲類神經網路(Time-Delay Neural Network)、粗糙集合(Rough Set)以及隨機森林(Random Forest)。實驗結果顯示,本文所提出的六種機器學習方法,都有不錯的辨識表現;其中,時間延遲類神經網路的辨識效能最佳。
In recent year, the integration of statistical process control (SPC) and engineering process control (EPC) has widely used in the industrial processes. Although the integration of SPC and EPC has a great benefit to a process, it causes the problem of recognition of control chart patterns (CCP). That is, EPC is able to compensate for the underlying disturbance; however, it may embed the effects of underlying disturbances. As a result, it becomes much more difficult to recognize the CCP. The recognition of CCP is crucial for the process improvement since those patterns are usually associated with some specific assignable causes. Accordingly, the issue of rapid and correct recognition of CCP for a SPC-EPC process is a very promising research topic in the industry. There has been little research conducted on the recognition of CCP for a SPC-EPC process so far. This study is motivated to propose six machine learning approaches to recognize the mixture patterns of process disturbances. Those six approaches include the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), time-delay neural network (TDNN), rough set (RS) and random forest (RF). Experimental results reveal that the proposed TDNN scheme has the best performance to recognize the CCP for an SPC-EPC process.
第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 研究流程與架構 3
第貳章 文獻探討 5
第一節 統計製程管制與工程製程管制 5
第二節 管制圖型樣辨識 6
第三節 機器學習法 7
第四節 本文與其它文獻之差異 8
第參章 研究方法 9
第一節 工程製程管制 9
第二節 管制圖型樣 13
第三節 類神經網路 20
第四節 支援向量機 23
第五節 極限學習機 28
第六節 時間延遲類神經網路 31
第七節 粗糙集合 33
第八節 隨機森林 34
第肆章 實證分析 36
第一節 資料敘述 36
第二節 建立模型及辨認結果 37
第三節 比較數種機器學習方法辨認結果 51
第伍章 結論及建議 55
第一節 研究發現 55
第二節 未來研究方向與建議 56
參考文獻 57
附 錄 63

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