博碩士論文 973203102 詳細資訊




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姓名 楊智宇(Chih-Yu Yang)  查詢紙本館藏   畢業系所 機械工程學系
論文名稱 希爾伯特-黃變換(Hilbert-Huang Transform)結合主成份分析與類神經網路在齒輪故障程度之診斷
(Application of Hilbert-Huang Transform to the Gear Fault Level Diagnosis Based on Principal Component Analysis and Neural Network)
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摘要(中) 本研究利用希爾伯特-黃變換(Hilbert-Huang Transform)針對齒輪箱的定轉速實驗進行故障診斷,並以時頻分析來判斷故障嚴重程度的大小。針對齒輪箱常見的故障:斷齒、磨損以及質量不平衡等等,先將複雜訊號分解成若干個IMF,再以包絡線分析從中提取故障特徵,作為診斷的依據。
  將時域訊號及HHT分析的結果中提取若干個特徵,利用主成份分析法將維度化簡,得到簡化後的綜合指標。將綜合指標當作類神經網路分類的輸入,分析的結果顯示,透過主成份降維後可以提高類神經網路的準確率。
摘要(英) In this study, Hilbert-Huang Transform (HHT) is utilized for fault diagnosis under fixed rotating speed. The time-frequency analysis is to identify the severity of the gear faults. The experimental cases include the common faults of the gearbox, such as broken teeth, gear wearing and gear unbalance. The complicated vibration signals due to faults are first decomposed into a number of Intrinsic Mode Functions (IMFs), and then the envelope analysis is employed to extract the fault characteristics.
  Specific features of time-domain signals as well as the results of HHT analysis are extracted for Principal Component Analysis (PCA) to achieve the characteristic dimension reduction. The composite indicators obtained from PCA are used as the inputs of Neural Network to classify the different gear faults. The analysis results show that through PCA, the characteristic dimension can be reduced and the classifying accuracy of neural network can be also improved.
關鍵字(中) ★ 類神經網路
★ 主成份分析
★ 嚴重程度
★ 故障診斷
★ 齒輪箱
★ 希爾伯特-黃變換
關鍵字(英) ★ Hilbert-Huang Transform
★ gearbox
★ fault diagnosis
★ Principa lComponent Analysis
★ Neural Network
論文目次 摘要 I
Abstract II
誌謝 III
目錄 III
圖目錄 VII
表目錄 XI
第一章 緒論 1
1-1 前言 1
1-2 研究動機 2
1-3 文獻回顧 4
第二章 希爾伯特-黃變換理論 10
2-1 希爾伯特-黃變換(Hilbert-Huang Transform, HHT) 10
2-2 瞬時頻率(Instantaneous Frequency) 10
2-3 固有模態函數(Intrinsic Mode Functions, IMF) 13
2-4 經驗模態分解法(Empirical Mode Decompostion, EMD) 14
2-5 希爾伯特時頻譜(Hilbert Spectrum)與邊際頻譜(Marginal Spectrum) 20
2-6 集成經驗模態分解法(Ensemble Empirical Mode Decompostion, EEMD) 21
2-7 後處理集成經驗模態分解法(Post processing of EEMD) 22
2-8 包絡線分析(Envelope Analysis) 24
2-8-1 振動訊號之調制(Modulation)與解調(Demodulation) 24
2-8-2 齒輪的振幅調制與頻率調制 25
2-8-3 調制訊號之包絡線分析方法 26
2-8-4 故障診斷之包絡線分析法 27
第三章 資料分析方法 29
3-1 主成份分析(Principal Component Analysis, PCA) 29
3-1-1 主成份分析原理 29
3-1-2 主成份分析的數學模型 31
3-1-3 主成份分析計算步驟 32
3-2 類神經網路(Artificial Neural Network) 35
3-2-1 類神經網路架構 36
3-2-2 學習規則 37
3-2-3 倒傳遞(Backpropagation)神經網路 37
第四章 實驗架構與實驗方法 41
4-1 齒輪故障類型 41
4-1-1 正常狀態 41
4-1-2 斷齒 42
4-1-3 齒輪磨損 43
4-1-4 齒輪質量不平衡 44
4-2 齒輪的振動機制 45
4-3 實驗架構 45
4-3-1 實驗平台 45
4-3-2 實驗設備與規格 46
4-4 實驗方法 51
4-5 頻率響應實驗 54
第五章 實驗結果 55
5-1 故障訊號處理及分析 55
5-1-1 齒輪正常狀態 56
5-1-2 齒輪斷齒狀態 66
5-1-3 齒輪磨損狀態 71
5-1-4 齒輪質量不平衡狀態 79
5-2 故障嚴重程度比較 87
5-3 維度化簡 89
5-3-1 特徵抽取 89
5-3-2 利用主成份分析法降維 92
5-4 類神經網路分類 94
第六章 結論與未來展望 104
參考文獻 106
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指導教授 吳天堯、黃衍任
(Tian-Yao Wu、Yean-Ren Hwang)
審核日期 2011-7-26
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