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研究生: 黃詩峰
Huang, Shih-Feng
論文名稱: 以原型網路實現小資料學習應用於永磁同步馬達故障診斷
Faults Diagnosis of Permanent Magnet Synchronous Motor With Few Shot Learning Based on Prototypical Neural Networks
指導教授: 謝旻甫
Hsieh, Min-Fu
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 95
中文關鍵詞: 永磁同步馬達預防性故障診斷原型神經網路小資料學習
外文關鍵詞: Permanent Magnet Synchronous Motor, Fault Diagnosis, Prototypical Neural Network, Few-shot Learning
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  • 摘要 I 致謝 XXIII 目錄 XXIV 圖目錄 XXVI 表目錄 XXVIII 符號表 XXIX 第一章 緒論 1 1.1 研究背景 1 1.2 文獻回顧 5 1.2.1模型基底診斷 5 1.2.2人工智慧檢測方法 7 1.3 研究動機與目的 10 1.4 論文架構 12 第二章 永磁同步馬達故障模型之模擬與分析 13 2.1 永磁同步馬達數學模型 13 2.2 定子匝間短路與轉子退磁故障特徵信號分析 15 2.2.1定子繞組之匝間短路故障數學模型 15 2.2.2匝間短路定子頻譜特徵 18 2.2.3轉子磁鐵之退磁故障說明 19 2.2.4轉子退磁故障頻譜特徵 21 2.3 故障馬達特徵有限元素分析 22 2.4 小結 28 第三章 基於原型網路之小資料學習故障診斷方法應用 30 3.1 尺度學習與小資料學習概念 32 3.1.1尺度學習簡介 32 3.1.2神經網路之特徵提取與預訓練 34 3.1.3嵌入函數與尺度學習過程 35 3.1.4尺度學習對於小資料學習之優勢 38 3.2 基於原型網路之小資料學習介紹 38 3.2.1批次任務 40 3.2.2嵌入函式:卷積神經網路 41 3.2.3嵌入函式:ResNet CNN神經網路 45 3.2.4原形網路 46 3.2.5原型網路之訓練方法 49 3.3 小結 50 第四章 馬達故障模型設計與實測結果 51 4.1 模組化故障馬達介紹 51 4.1.1模組化定子繞組介紹 52 4.1.2退磁轉子模組介紹 54 4.2 永磁同步馬達實測規劃 57 4.2.1實測驗證平台介紹 57 4.2.2實驗規劃 59 4.3 故障狀態下相電流電流幅值變化 60 4.3.1無載狀態電流幅值 61 4.3.2有載狀態電流幅值 61 4.3.3故障狀態對相電流幅值之影響 62 4.4 故障狀態下相電流電流諧波成分比較 63 4.4.1無載狀態下相電流電流諧波成分比較 63 4.4.2有載狀態下相電流電流諧波成分比較 64 4.4.3故障狀態下對相電流電流諧波成分之影響 65 4.5 小結 66 第五章 原型網路應用匝間短路及退磁故障診斷 68 5.1 深度學習數據前處理 68 5.2 深度神經網路運作環境:PyTorch 74 5.3 實驗案例與結果分析 75 5.3.1輕微故障之預防性診斷 76 5.3.2精確故障程度診斷 80 5.3.3新型故障延伸診斷 82 5.4 與其他機器學習比較 85 5.5 小結 88 第六章 結論與未來展望 89 6.1 結論 89 6.2 未來展望 90 參考文獻 91

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