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研究生:徐子軒
研究生(外文):HSU, TZU-HSUAN
論文名稱:以預測與健康管理(PHM)技術應用於航空器起落架精進維修之研究
論文名稱(外文):Improve Maintenance of Aircraft Landing Gear with Prognostics and Health Management (PHM)
指導教授:張淵仁張淵仁引用關係
指導教授(外文):CHANG, YUAN-JEN
口試委員:李傑劉素卿張淵仁
口試委員(外文):Jay LeeCHANG, YUAN-JEN
口試日期:2022-06-27
學位類別:碩士
校院名稱:逢甲大學
系所名稱:數據科學碩士學位學程
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:2022
畢業學年度:110
語文別:中文
論文頁數:76
中文關鍵詞:起落架預測與健康管理剩餘可用壽命故障診斷預測性維修
外文關鍵詞:landing gearprognostics and health management (PHM)remaining useful life (RUL)fault diagnosispredictive maintenance (PdM)
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起落架是飛機一項重要的系統。然而,起落架部件的壽命在其操作期間容易退化,這可能導致在起飛和著陸期間發生的擺振現象。為了提高飛機的妥善率以及減少計劃外的出勤中斷,本研究以航空器起落架為研究標的,導入預測與健康管理(PHM)技術。在相關限制下運用可掌握的歷史數據,依PHM方法論,建置起落架剩餘可用壽命(RUL)預測模型與故障診斷模型,期望給予維運單位預測性維護建議。利用此模型可以進行預測性維護排程,精準備料,達成適質、適量及降低成本的目的,並避免起落架失效所導致人、機安全問題。
The landing gear is an essential part of an aircraft. However, the components of landing gear are susceptible to degradation over the life of their operation, which can result in the shimmy effect occurring during take-off and landing. In order to reduce unplanned flight disruptions and increase the availability of aircraft, this study introduces prognostics and health management (PHM) technique with aircraft landing gear as the research target. By using the available historical data under the relevant restrictions, the remaining useful life (RUL) prediction model and fault diagnosis model of the landing gear are constructed according to the PHM methodology, and it is expected that the maintenance unit will be given the predictive maintenance suggestions. The model can be used to carry out predictive maintenance scheduling, precise spare parts demand, achieve the purpose of appropriate quality, appropriate amount, and cost reduction, and avoid a fatal accident caused by landing gear failure.
第一章 緒論 1
第一節 研究動機與目的 2
第二節 研究標的介紹與問題描述 4
1.2.1 研究標的介紹:起落架與擺振效應 4
1.2.2 問題描述 6
第三節 論文架構 9

第二章 文獻探討 10
第一節 起落架擺振效應 10
第二節 目前的維護方法與隱憂 12
第三節 大數據在航太領域的應用 15
第四節 在服役中飛機上導入新數位技術的挑戰 16

第三章 研究方法 18
第一節 基本理論 19
3.1.1 預測與健康管理 19
3.1.2 費雪準則 20
3.1.3 主成分分析 21
3.1.4 邏輯回歸 22
3.1.5 ARIMA時間序列模型 24
3.1.6 支援向量機 25
3.1.7 交叉驗證 27
第二節 方法流程 28
3.2.1 數據撷取 29
3.2.2 資料前處理 31
3.2.3 特徵提取 32
3.2.4 特徵篩選 34
3.2.5 模型建立 36
3.2.5.1 健康指標模型 36
3.2.5.2 RUL預測模型 36
3.2.5.3 故障診斷模型 38

第四章 研究結果與討論 39
第一節 數據取得與前處理 40
第二節 特徵提取 41
第三節 健康評估模型 43
4.3.1 特徵篩選 43
4.3.2 模型訓練 49
4.3.3 模型評估 51
第四節 剩餘可用壽命預測 54
4.4.1 模型訓練 56
4.4.2 模型評估 58
第五節 故障診斷模型 64
4.5.1 特徵篩選 64
4.5.2 模型訓練 64
4.5.3 模型評估 66

第五章 結論與建議 68
第一節 結論 68
第二節 未來研究建議 69
參考文獻 71


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