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研究生:黃榮吉
研究生(外文):Rong-Ji Huang
論文名稱:應用資料探勘與本體論之半導體設備遠端診斷系統之開發
論文名稱(外文):Development of an e-Diagnostics System for Semiconductor Equipment Using Data Mining and Ontology
指導教授:洪敏雄洪敏雄引用關係鄭芳田鄭芳田引用關係
指導教授(外文):Min-Hsiung HungFan-Tien Cheng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:製造工程研究所碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:114
中文關鍵詞:網路服務本體論資料探勘
外文關鍵詞:Web-ServicesOntologye-Diagnostics
相關次數:
  • 被引用被引用:18
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:5
本論文利用資料探勘 (Data Mining)、本體論 (Ontology) 及網路服務 (Web-Services) 等技術發展了一套半導體設備遠端診斷系統 (Remote Diagnostics System),以期縮短機台設備錯誤診斷及故障排除的時間。首先,我們設計了一個資料探勘系統,以對設備商端(Supplier Side)使用者回覆的所有診斷修復資訊進行探勘,進而建立一個診斷解答資料庫(Diagnostics-Solutions Database),如此,系統即可根據客戶端送來的機台錯誤描述資訊,快速地找出相對應的診斷解答。其次,我們利用Ontology、OIL (Ontology Inference Layer)及RDF(Resource Description Language)等技術來建立機台設備的診斷知識模型,並利用XML-binding技術建立了診斷知識資料庫(Diagnostics Knowledge Database),藉此,系統可以根據機台的錯誤描述資料,找出與此錯誤相關的所有診斷資訊。接著,我們整合上述兩個系統,建立一個診斷解決方法的擷取系統 (Diagnostics-Solutions Retrieval System, DSRS),並使用網路服務 (Web-Services)等分散式物件技術,來提供工廠端(Factory Side)顧客使用此一系統之服務。最後,我們建構一個電子診斷應用實例,以進行系統的整合與測試,並驗證本遠端診斷系統之效能。
In this thesis, we use Data Mining, Ontology, and Web-services technologies to develop a remote diagnostics system for semiconductor equipment. The proposed diagnostics system is invented to effectively shorten the diagnosis and error-recovery time of equipment. First, a data-mining system is designed to mine the diagnostics-solutions data that are replied to and stored at the supplier site by factory clients so as to create a diagnostics-solutions database. The data-mining system can quickly reply the associated diagnostics solutions as it receives fault descriptions from the client. Next, we use Ontology, OIL (Ontology Inference Layer), and RDF (Resource Description Language) technologies to construct a diagnostics knowledge model for semiconductor equipment. Accordingly, a diagnostics knowledge database is created using the XML-binding technique. Given a specific fault description, the Ontology system can provide all of the relative diagnostics solutions. Then, we combine the data-mining system and the Ontology system to form a diagnostics-solutions retrieval system, called DSRS, which can provide diagnostics solutions to factory clients through Web-services technologies. Finally, we construct an application paradigm of e-diagnostics and develop the associated procedures of system integration and testing to evaluate the effectiveness of the proposed remote diagnostics system.
中文摘要
英文摘要
致謝

目 錄 I
圖 目 錄 IV
表 目 錄 VII
第一章 緒論 1
  1.1 研究背景與動機 1
  1.2 研究目的 6
  1.3 論文架構 8
第二章 理論基礎 9
  2.1 資料探勘 (Data Mining) [7][28][29] 9
    2.1.1 資料探勘 (Data Mining) 與資料倉儲 (Data Warehouse) [31] 11
    2.1.2 資料探勘 (Data Mining) 與線上分析處理 (OLAP) [31] 12
    2.1.3 資料探勘 (Data Mining) 的分析法則 [28] 12
  2.2 本體論 (Ontology) [19][32] 14
  2.3 Ontology Inference Layer (OIL) [33] 15
  2.4 OIL與網路規範標準 18
    2.4.1 XML [35] 19
    2.4.2 DTD 與 XML Schema [37] 21
    2.4.3 RDF [20] 24
    2.4.4 Using RDF to Represent OIL 26
  2.5 XML-Binding [27][39] 28
  2.6 分散式物件 [40]~[46] 29
  2.7 UML [47] 33
  2.8 物件導向系統的發展程序 35
第三章 診斷資訊擷取系統 38
  3-1 資料探勘系統(Data Mining System) 38
    3-1-1 設計步驟 38
    3-1-2 資料篩選與轉換機制 38
    3-1-3 資料探勘引擎(Data-Mining Engine) 39
    3-1-4 建立診斷解答資料庫 45
  3-2 本體論系統(Ontology System) 47
    3-2-2 定義診斷修護知識模組 48
    3-2-3 建構診斷修護知識之OIL及RDF檔案 51
    3-2-4 診斷修護知識之 XML Schema 設計 53
    3-2-5 建立診斷知識資料庫欄位 55
  3-3 DSAS之實作及測試 55
第四章 以資料探勘與本體論為基礎之設備錯誤診斷系統 60
  4-1 前言 60
  4-2 發展程序 63
  4-3 RDS 的需求分析 66
  4-4 發展 DSRS 66
  4-5 發展RDS Communication Manager 66
  4-6 定義RDS Framework Messages 69
  4-7 發展RDS系統 76
    4-7-1 物件導向分析之使用者案例圖 76
    4-7-2 RDS 的循序圖 78
    4-7-3 RDS 的類別圖 85
  4-8 系統OOD的類別圖及循序圖 86
第五章 應用實例建構及系統整合測試 87
  5.1 診斷系統的整合 87
第六章 結論 96
參考文獻 98
附錄 A OIL及RDF檔 102
附錄 B OOD之順序圖 108
附錄 C 診斷知識之Schema檔 112


圖 目 錄
圖1.1 半導體設備投資比例 [1] 1
圖1.2 機台使用效能 [1] 2
圖1.3 e-Diagnostics Overview [2] 3
圖1.4 整合DSRS與WSDF之半導體設備遠端診斷系統 7
圖2.1 KDD Process [28] 10
圖2.2 Data Mining 與Data Warehouse的關係 [30] 11
圖2.3 OLAP 與Data Mining的關係 12
圖2.4代理人運用本體論機制溝通 [21] 15
圖2.5 以OIL語法描敘物件導向類別圖 18
圖2.6在不同平台上顯示同一份XML文件 19
圖2.7 HTML 與 XML語法描敘範例 20
圖2.8 Document Type Definition (DTD) 22
圖2.9 XML Schema 23
圖2.10 RDF基本資料模式 25
圖2.11 (a) 以RDF標示圖描述XML 26
圖2.11 (b) 以RDF語法描述XML 26
圖2.12 以OIL語法描敘物件導向類別圖 27
圖2.13 以RDF語法描敘物件導向類別圖 27
圖2.14 XML Data-Binding 機制 [27][37] 28
圖2.15 Client and Server Communicate through RMI [42] 30
圖2.16 Client and Server Communicate through CORBA [42] 31
圖2.17 Client and Server Communicate through DCOM [42] 31
圖2.18 Web Services 示意圖 33
圖2.19 Development Procedure for Object-Oriented Systems 37
圖3.1 資料的篩選與轉換 39
圖3.2 Apriori 演算法[28] 40
圖3.3 Apriori 範例[28] 42
圖3.4 資料庫語法 43
圖3.5 與斷線相關之診斷修復資訊 44
圖3.6 不同組合的診斷修復資訊 44
圖3.7 排序後輸出診斷修復資訊 44
圖3.8 診斷解答資料庫建立程序 45
圖3.9 DBMiner編輯器 46
圖3.10 本體論系統之階層關係圖 47
圖3.11 The ontology development process [55] 49
圖3.12 機台修護模組類別圖 50
圖3.13建構診斷修護知識之OIL及RDF檔案 52
圖3.14 機台修護模組樹狀結構圖 53
圖3.15 診斷修護資訊的資料結構 54
圖3.16 診斷知識資訊資料表 55
圖3.17 Web-Services之監控畫面 57
圖3.18 Supplier 管理領域知識介面 58
圖3.19 DAS 的控制介面 59
圖3.20 診斷結果回覆報告介面 59
圖4.1 Web-Services-based Diagnostics Framework[26] 61
圖4.2 遠端診斷系統示意圖 62
圖4.3 遠端診斷系統發展流程 64
圖4.4 Web-Services Agent Architecture [26] 68
圖4.5 Web-Services Agent類別圖 [26] 69
圖4.6 遠端診斷系統架構圖 71
圖4.7 Local Diagnostic Process. 72
圖4.8 Remote Diagnostic Process. 74
圖4.9 RDS 之使用者案例圖 76
圖4.10 診斷問題之順序圖 79
圖4.11 查詢機台資訊之順序圖 80
圖4.12 登入之順序圖 81
圖4.13 維護Knowledge Object Model資訊之順序圖 82
圖4.14 維護Knowledge Entities資訊之順序圖 83
圖4.15 維護Diagnostics Solution Database資訊之順序圖 84
圖4.16 RDS 的類別圖(OOA) 85
圖4.17 RDS 的類別圖(OOD) 86
圖5.1 遠端診斷系統流程圖 89
圖5.2 Stage I 診斷流程圖 90
圖5.3 Stage II 診斷流程圖 91
圖1(a):以 OIL 語法表示圖 3.7 機台修護模組類別 102
圖1(b):以 RDF 語法轉換圖1(a)的OIL敘述 104
圖B1 診斷問題之順序圖 (OOD) 108
圖B2 查詢機台資訊之順序圖 (OOD) 109
圖B3 登入之順序圖 (OOD) 109
圖B4 維護 Knowledge Object Model 資訊之順序圖 (OOD) 110
圖B5 維護 Knowledge Entities 資訊之順序圖 (OOD) 110
圖B6 維護 Diagnostics Solution Database 資訊之順序圖 (OOD) 111


表 目 錄
表一、 各診斷方法之比較 5
表二、 XML Schema與DTD之比較 [38] 23
表三、 DSRS 遠端診斷系統之軟硬體需求規格 56
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