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研究生: 羅鈺洋
Yu-Yang Luo
論文名稱: 基於電訊號與派克圓之三相感應馬達故障診斷與多重損壞分解
Fault Diagnosis and Multiple Faults Decomposition of Three-phase Induction Motor Based on Electrical Signals and Park’s Circle
指導教授: 劉孟昆
Meng-Kun Liu
藍振洋
Chen-Yang Lan
口試委員: 劉益宏
Yi-Hung Liu
學位類別: 碩士
Master
系所名稱: 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 128
中文關鍵詞: 感應馬達電壓電流故障診斷機器學習派克圓多重損壞分析奇異值分解
外文關鍵詞: induction motor, voltage and current, fault diagnosis, machine learning, Park’s circle, multiple fault analysis, singular value decomposition
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  • 摘要 Abstract 誌謝 目錄 表目錄 圖目錄 第一章 緒論 1.1 前言 1.2 文獻回顧 1.2.1 單一損壞訊號診斷 1.2.2 多重損壞訊號診斷 1.2.3 訊號分析 1.2.4 特徵處理 1.2.5 機器學習 1.2.6 奇異值分解 1.3 論文架構 第二章 研究方法 2.1 訊號分析 2.1.1 離散傅立葉轉換(discrete-Fourier transform, DFT) 2.1.2 離散小波轉換(discrete wavelet transform, DWT) 2.1.3 派克向量法(Park’s vector approach, PVA) 2.1.4 擴展派克向量法(extended Park’s vector approach, EPVA) 2.2 特徵預處理 2.2.1 正規化(normalization) 2.3 特徵處理 2.3.1 費雪分數(Fisher’s score, F-score) 2.4 機器學習演算法(machine learning algorithm) 2.4.1 決策樹(decision tree, DT) 2.4.2 隨機森林(random forest, RF) 2.4.3 k-近鄰演算法(k-nearest neighbor, kNN) 2.4.4 人工神經網路(artificial neural network, ANN) 2.4.5 支持向量機(support vector machine, SVM) 2.4.6 格點搜尋法(grid search) 2.4.7 Add-one-feature-in strategy 2.4.8 K 折交叉驗證(K-fold cross-validation, K-fold CV) 2.5 奇異值分解(singular value decomposition, SVD) 第三章 實驗規劃 3.1 實驗設備 3.2 實驗流程 3.2.1 基於特徵分析 3.2.2 基於派克圓分析 3.3 損壞類型 第四章 分析與分類結果 4.1 單一損壞之馬達訊號分析結果 4.2 基於 SVM 分類多重損壞 4.3 基於 SVD 分類多重損壞 第五章 結果討論與未來展望 5.1 結果討論 5.2 研究貢獻 5.3 未來展望 第六章 參考文獻 附錄A 特徵種類 附錄B 其他分類器之特徵處理與分類結果 附錄C 基於 SVM 分類其他多重損壞之結果 附錄D 左奇異向量內積之結果 附錄E 基於 SVD 分類其他多重損壞之結果 附錄F 挑選左奇異向量後分類其他多重損壞之結果

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