臺灣博碩士論文加值系統

以燃氣渦輪(Gas Turbine)發動機做為基本動力機械裝置平台，已廣泛應用於輕工業、重工業、航空、航海等動力裝備。我國先進之國防武器中，許多戰機均使用這類機械裝置做為動力來源，機上也安裝多種的偵測器隨時監測發動機的運作狀態。此類發動機的維修方式多採用定期檢測與維修方式進行，但對於長時間處在高溫與高機械應力操作環境下的發動機而言，定期檢修的方式對於因組件傷害的累積所造成的發動機性能衰降，無法提供有效的性能預測藉以提高使用此類發動機器具使用的安全性。因此，本研究蒐集此類發動機相關的維修與飛行操作紀錄，嘗試運用資料探勘技術建立一個具有最低成本的發動機失效即時預測模式，提供維修人員發動機失效的預警，進而規劃適切之維修計畫，用以降低維修零組件的庫存與人力需求。本研究利用發動機性能衰降的歷史紀錄以及四種資料探勘技術建立預測模式，用以預測發動機性能衰降後之剩餘可用時間。本研究比較四種預測模式的良窳，提供建立發動機性能衰降預測模式的建立架構，期能讓傳統的定期維修升級為預兆式的維修管理，以提供良好的發動機使用安全。

Use of a gas turbine engine as the primary power source has been popular in light and heavy industries, aerospace engineering, marine engineering, etc. Gas turbine engine is also used in our modern national defense weapons in Taiwan. For instance, most of Air Force fighters use gas turbine engines as the source of power. Gas turbine engines are usually associated with various sensors for real-time condition surveillance and require periodical maintenance for providing proper functioning and safety guarantee. In contrast, real-time failure prediction of gas turbine engine components could be achieved by applying data mining or statistics techniques. However, such failure prediction will not be effective when applying to engines which are deteriorated by long-term running in high temperature and high stress environment. In this study, we collected maintenance and operating logs according to the engine deterioration history and established and empirically evaluated four different data-mining-based prediction models. The proposed data-mining-based prediction approach attempts to predict the time-to-deterioration for a gas turbine engine after its prior deterioration occurrence, to provide maintenance personnel accurate prediction for better making or revising maintenance schedules, and to achieve the “foreseeing maintenance and management policy.”

第一章 緒論
第一節 研究背景 1
第二節 研究動機與目的 4
第三節 論文結構 5
第二章 文獻探討
第一節 發動機性能衰降因素 6
第二節 飛機發動機組件更換預測 8
第三節 估計發動機組件失效時間的預測技術 11
第三章 蒐集及處理發動機性能衰降資料過程 17
第四章 實證評估
第一節 資料來源 21
第二節 模式效能評估準則 22
第三節 評估程序 24
第四節 預測結果與分析 26
第五節 討論 44
第五章 結論
第一節 研究結論及貢獻 46
第二節 未來研究方向 47
參考文獻
一、中文部份 48
二、英文部份 48

一、中文部份
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