臺灣博碩士論文加值系統

機台（Equipment）是半導體廠中投入最多資本的資產（Asset），約佔總投資金額的75% ，然而，根據統計，如此昂貴的機台設備卻只有33%的機台使用效能（Overall Equipment Effectiveness，OEE），有太多的時間，機台是處於閒置狀態，亦或進行排定與非排定的維修。本論文提出一機台預防保養之預兆偵測模型（Prognostic Model），以期可在機台尚未發生故障前，即可預測出機台未來可能之錯誤，機台維修人員可據以事先採取必要之防範措施，進而有效提高機台的使用效能與可用率（Availability）。
首先，我們針對機台設備的加工行為模式與物理特性做深入研究與分析，藉以瞭解預測模型輸入輸出之間的關係。然後利用倒傳遞類神經網路（Back-Propagation Neural Network）與時間序列分析觀念（Time Series Analysis），完成預防保養之預兆偵測模型之建構。接著我們設計網路式預防保養架構中各功能元件，並使用網路服務（Web Services）等分散式物件技術實作。此外，負責擷取機台工程資料（Equipment Engineering Data）之通用型嵌入式裝置（Generic Embedded Device）也一併設計於本架構中。最後，我們建構一個網路式預防保養應用實例，同時提出其相配合之系統整合與測試步驟，以驗證本架構之效能。

Equipment is the most expensive asset in the semiconductor factory. The cost of equipment is in a total of seventy-five percent of the capital investment. However, according to statistics, such an expensive equipment usually only has thirty-tree percent overall equipment effectiveness (OEE). It takes too much time for equipment to stay in idle statuses or scheduled and unscheduled maintenance. In this paper, a prognostic model for equipment maintenance is proposed. The prognostic model can predict the future potential faults of equipment before they occur. Accordingly, equipment engineers can take necessary precautions against those faults in advance. Consequently, the availability of equipment can be effectively raised.
First, we investigate the behavioral modes and the physical characteristic of equipment to understand the relationships between the inputs and the outputs of the prognostic model. Next, Back-Propagation Neural Network (BPN) and Time Series Analysis are used to contrast the prognostic model. Then, the functional components of the e-Maintenance functional schemes are designed and implemented with distributed object-oriented technologies, such as UML, Web Services, etc. In addition, a generic embedded device (GED) that can for acquire equipment-engineering data is also designed in the functional scheme. Finally, we construct an application paradigm of e-maintenance and develop the associated procedures of system integration and testing to evaluate the effectiveness of the proposed e-Maintenance functional schemes.

中文摘要
英文摘要
誌謝
目 錄I
圖 目 錄III
表 目 錄V
第一章、緒論1
1.1研究之背景描述1
1.2研究動機與目的2
1.3論文架構9
第二章、理論基礎10
2.1簡單統計基礎10
2.1.1平均值與標準差（mean and standard deviation）10
2.1.2共變數（covariance）12
2.1.3相關係數13
2.1.4迴歸分析14
2.2時間序列分析16
2.2.1自我相關係數16
2.2.2自我相關函數16
2.2.3移動平均法17
2.3類神經網路19
2.3.1倒傳遞網路（BPN, Back Propagation Network）24
2.3.2倒傳遞網路架構24
2.3.3倒傳遞網路演算法25
2.3.4倒傳遞網路演算法的運作流程28
2.4統一塑模語言（UML）29
2.4.1物件導向系統的發展程序30
第三章、預防保養之預兆偵測模型的設計與建構33
3.1領域知識研究33
3.1.1系統簡介33
3.1.2濺鍍技術說明35
3.1.3問題描述35
3.2預兆偵測模型之建構36
3.2.1推估模式類神經網路之建立37
3.2.2預測模式建立50
3.3預兆偵測模型績效評估59
3.3.1推估模式類神經網路之效能評估60
3.3.2預測模式之效能評估64
3.3.3預兆偵測模型之效能評估68
3.4預防保養之預兆偵測模型的應用74
第四章、網路式預防保養功能架構之分析與設計75
4.1功能架構設計75
4.2物件導向分析（OOA）82
4.2.1使用者案例圖（Use Case Diagram）82
4.2.2物件導向分析之循序圖（OOA）84
4.2.3物件導向分析之類別圖（OOA）93
4.3物件導向設計（OOD）96
第五章、應用實例之建構與整合測試99
5.1架構實作99
5.1.1開發環境99
5.1.2實作步驟100
5.1.3圖形使用者介面101
5.2網路式預防保養架構應用實例102
5.3架構整合測試103
第六章、結論104
6.1論文總結104
6.2研究成果105
6.3未來研究方向106
參考文獻107

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