臺灣博碩士論文加值系統

傳統之故障診斷方式多半依賴維護人員的專業知識與經驗判斷，因此維護工作易受人員知識、經驗不足，甚至情緒所影響，而個人的知識、經驗難以系統化與傳承，更是增加了設計診斷系統的困難度。
本論文提出一個基於本體論的故障診斷架構，透過本體論與邏輯推論技術，提供較有效且精確的故障診斷資訊。本論文中詳細描述了如何將診斷過程中所需要的有關知識，例如：診斷工作、領域、解決問題方法等設計為數個各自獨立的本體論，並利用本體論轉接器、本體論連結器連結各個本體論，以減少彼此的相依關係，提高其再用性。本論文中也探討了故障現象與故障原因的關係，並利用權重值的設定及邏輯命題的推論，來達成故障診斷的工作。
此外，本論文也結合學習的機制，藉累積過去發生故障的歷史經驗，適切地調整故障現象與原因之間的權重值，產生與設備相關的專屬故障資訊，來得到更精確的診斷。當產生新的故障資訊時，更可透過更新知識庫的方式來使得診斷工作更符合設備之實際狀況，有效提高故障診斷的成效。

Traditional diagnosis methods mostly depend on people’s knowledge and experience, and that makes maintenance affected by people. Besides, it will increase the difficulty of designing a diagnosis system, because it’s tough to systemize people’s experience and professional knowledge.
In this thesis, we propose an ontology-based diagnosis architecture and provide more efficient and precise diagnosis information by use of logic inference. We describe how to design ontologies representing relevant knowledge about a domain, a task, and a problem-solving method. In addition, an ontology linker and an ontology adapter are used for connecting ontologies to reduce the interdependence among ontologies and to increase reusability. We also explore the relationship between symptoms and causes and finish a diagnosis task by setting weight and applying logic inference.
Furthermore, a learning mechanism is applied to accumulate previous diagnostic histories and adjust the weight to produce equipment-specific information and make a more precise diagnosis. By updating the knowledge base, the architecture can provide an appropriate diagnosis when getting new fault information about the equipment.

摘 要 I
ABSTRACT II
誌 謝 III
目 錄 IV
圖 目 錄 VI
表 目 錄 VIII
第一章 緒論 1
1.1 背景 1
1.2 動機 1
1.3 研究方法 3
1.4 論文架構 4
第二章 相關技術 5
2.1本體論 5
2.1.1本體論的組成與分類 6
2.1.2 本體論的整合 8
2.1.3 建構本體論的方法 9
2.1.4 建構本體論的工具 10
2.1.5 領域繼承及領域階層 14
2.2 邏輯推論 15
2.3問題解決方法論 19
第三章 系統架構 25
3.1 概述 25
3.2 架構圖 25
3.3 表示符號說明 26
3.4 本體論 29
3.4.1 領域本體論 29
3.4.2 工作本體論 34
3.4.3 方法本體論 36
3.4.4 本體論連結器 41
3.4.5 本體論轉接器 50
第四章 實例推導 52
4.1關係矩陣的建立 52
4.2 例子的說明 53
4.3 討論 61
第五章 結論及未來研究方向 64
參 考 文 獻 67

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