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姓名	林祐聖(Yu-sheng Lin)  查詢紙本館藏	畢業系所	機械工程學系
論文名稱	硬體實現階次追蹤技術結合希爾伯特-黃轉換與振幅正規化於非固定轉速軸承故障診斷研究 (Roller Bearing Defect Identification under Variable Rotating Speed Using Hilbert-Huang Transform and Amplitude Normalization via Hardware Implemented Order-Tracking Technique)
相關論文	★ 應用希爾伯特黃變換(HHT)之邊際譜分析於旋轉機械的元件鬆脫故障診斷 ★ 總體經驗模態分解法(EEMD)結合自回歸(AR)模型在旋轉機械之元件鬆脫故障診斷之應用 ★ 希爾伯特-黃變換(HHT)在旋轉機械之軸承故障診斷的應用 ★ 希爾伯特黃變換(HHT)於變轉速之齒輪故障診斷之應用 ★ 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 ★ 結合經驗模態分解與多尺度熵分析之階次追蹤技術於非固定轉速之軸承故障診斷 ★ 運用主成分分析於加速規訊號模擬壓力中心之人體靜態平衡評估
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摘要(中)	本研究利用希爾伯特-黃轉換針對旋轉機械的軸承元件於變轉速下，軸承發生外圈損壞、內圈損壞、滾子損壞和複合故障等情形進行故障診斷。首先，利用階次追蹤方法將轉速變動因子抽離，使非穩態的訊號轉換成穩態的角度域訊號，再提取角度域訊號之包絡線透過希爾伯特-黃轉換進行分析，於分解出來的固有模態函數和希爾伯特邊際譜探討軸承不同的損壞特徵，並經由振幅正規化後使得故障特徵不會隨著變轉速影響，最後，以支持向量機進行單一故障軸承的故障診斷，且利用此分類器對複合故障軸承進行故障診斷。
摘要(英)	In this study, Hilbert-Huang transform (HHT) is utilized for diagnosing the roller bearing faults, such as outer race defect, inner race defect, roller defect and multi-fault, under variable rotation speed. The vibration signals are first measure through the order tracking technique, so that the vibration signals are detected with identical angle increment and thus the vibration signals are stationary without the factor of variable shaft rotation speed. The envelope signals of the measurements are analyzed by Hilbert-Huang transform approach. The features of the faulted bearings are extracted by investigating the intrinsic mode functions (IMFs) as well as the marginal Hilbert spectra. The extracted features of the faulted bearing are then re-scaled through the amplitude normalization, so that the vibration energy are not affected by the variable rotation speed. Finally, the support vector machine is employed to identify the individual defect of bearing. The same SVM structure is also used to diagnose the occurrence of multi-fault in bearings.
關鍵字(中)	★ 希爾伯特-黃轉換 ★ 階次追蹤 ★ 振幅正規化 ★ 支持向量機 ★ 故障診斷	關鍵字(英)
論文目次	摘要 I Abstract II 目錄 III 圖目錄 VI 表目錄 XIV 第一章 緒論 1 1-1 前言 1 1-2 研究動機 2 1-3 文獻回顧 3 1-4 研究目的 6 第二章 理論 7 2-1 希爾伯特-黃轉換 7 2-1-1 固有模態函數 8 2-1-2 經驗模態分解法 8 2-1-3 集成經驗模態分解法(Ensemble EMD, EEMD) 9 2-1-4 集成經驗模態分解法之後處理過程(Post-Processing of EEMD) 10 2-1-5 瞬時頻率與瞬時振幅 11 2-1-6 希爾伯特時頻譜和邊際希爾伯特頻譜 12 2-2 包絡線分析 13 2-3 軸承損壞特徵 13 2-3-1 軸承特徵頻率與特徵階次 14 2-3-2 軸承訊號之包絡線特徵 16 2-4 支持向量機 18 2-4-1 超平面(hyper-plan) 18 2-4-2 支持向量(support vector) 19 2-4-3 核心函數(kernel function) 20 2-4-4 多分類支持向量機 21 第三章 實驗架構與實驗方法 22 3-1 實驗架構說明 22 3-2 實驗設備與規格 24 3-3 實驗之軸承故障類型 33 3-4 實驗設定 37 第四章 軸承故障特徵擷取方法 39 4-1 角度域分析 42 4-2 階次域分析 45 4-3 振幅正規化角度域分析 47 4-4 振幅正規化階次域分析 49 第五章 結果與討論 51 5-1 正常軸承 52 5-2 外圈損壞軸承 60 5-3 內圈損壞軸承 68 5-4 滾柱損壞軸承 76 5-5 單一故障軸承的支持向量機分類 84 5-6 特徵擷取方法比較 126 5-7 複合故障軸承的支持向量機分類 128 第六章 結論與未來展望 136 6-1 結論 136 6-2 未來展望 137 參考文獻 138
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指導教授	吳天堯	審核日期	2013-7-18
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