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研究生:陳仁傑
研究生(外文):Jen-chieh Chen
論文名稱:希爾伯特黃變換(HHT)於變轉速之齒輪故障診斷之應用
論文名稱(外文):Application of Hilbert-Huang Transform to Gear Fault Diagnosis under Variable Speed
指導教授:吳天堯黃衍任
指導教授(外文):Tian-yau WuYean-ren Hwang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:機械工程研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:106
中文關鍵詞:故障診斷齒輪訊號處理希爾伯特黃轉換
外文關鍵詞:fault diagnosisHilbert Huang transformsignal processinggear
相關次數:
  • 被引用被引用:13
  • 點閱點閱:871
  • 評分評分:
  • 下載下載:209
  • 收藏至我的研究室書目清單書目收藏:0
本論文主要目的是研究旋轉機械齒輪系統在變轉速的情況下,齒輪發生磨損、斷齒、不平衡等故障時,使用希爾伯特-黃轉換方法分析其非線性、非穩態訊號,以提取其故障特徵。首先將訊號透過集成經驗模態分解法分解,並進行後處理過程,得到數個的固有模態函數,使用正常化希爾伯特變換方法、一般化跨零點方法、Direct Quadrature方法,三種不同的方法計算每一個固有模態函數的瞬時頻率,將瞬時頻率計算結果利用馬達轉速進行無因次單位頻率正規化,使固有模態函數去除轉速的因子後,選取具有意義的固有模態函數,將其得到的無因次單位瞬時頻率和瞬時振幅作時間-頻率-能量分布圖,並對時間積分作頻率-能量圖,即為邊際頻譜圖,從四種實驗類型之頻譜圖中有效的找出對應之故障特徵,以辨別出四種故障類型。
The main purpose of this paper is to study the fault features of gear system, such as gear wearing, teeth broken, gear unbalance, under variable rotation speed. The Hilbert-Huang Transform (HHT) method is utilized to analyze the nonlinear and non-stationary vibration signals. The signals are decomposed into a number of Intrinsic Mode Function (IMF) through the Ensemble Empirical Mode Decomposition (EEMD) and the Post-Processing of EEMD. The three different methods of calculating the instantaneous frequencies, Normalized Hilbert Transform (NHT) method, Generalized Zero-Crossing (GZC) method, and Direct Quadrature (DQ) method, are employed to determine the instantaneous frequencies of IMFs. The dimensionless frequency-time-energy distributions of IMFs is then obtained through dimensionless frequency normalization, so that the factor of shaft rotation speed can be extracted. The characteristic dimensionless frequencies of different fault types can be identified in the marginal spectrum of the information-contained IMFs.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 xiii
第一章 緒論 1
1-1 研究背景與動機 1
1-2 文獻回顧 3
1-3 研究目的 5
1-4 本文大綱 6
第二章 理論 7
2-1 希爾伯特黃轉換(Hilbert-Huang Transform, HHT) 7
2-2 經驗模態分解法(Empirical Mode Decomposition, EMD) 7
2-2-1 固有模態函數(Intrinsic Mode Function, IMF) 8
2-2-2 EMD流程 8
2-3 集成經驗模態分解法(Ensemble EMD, EEMD) 11
2-4 集成經驗模態分解法之後處理過程(Post-Processing of EEMD) 12
2-5 瞬時頻率(Instantaneous Frequency) 12
2-5-1 正常化希爾伯特轉換(Normalized Hilbert Transform, NHT) 12
2-5-2 一般化跨零點(Generalized Zero-Crossing, GZC) 13
2-5-3 Direct Quadrature(DQ) 14
2-6 無因次單位頻率正規化(Dimensionless Frequency Normalization) 14
2-7 希爾伯特時頻譜和邊際希爾伯特頻譜 (Hilbert spectrum, Marginal Hilbert Spectrum) 15
第三章 齒輪故障類型 16
3-1 齒輪振動頻率 16
3-1-1 齒輪嚙合時的固有頻率 16
3-1-2 齒輪轉軸的旋轉頻率 16
3-1-3 齒輪的嚙合頻率 17
3-1-4 齒輪嚙合的邊頻帶 17
3-2 齒輪磨損故障 17
3-3 齒輪斷齒故障 18
3-4 齒輪不平衡故障 19
第四章 實驗架構及實驗方法 20
4-1 實驗架構說明 20
4-2 實驗設備裝置與規格 22
4-2-1 變頻器及轉速感測器 22
4-2-2 馬達 22
4-2-3 Keyphasor 23
4-2-4 聯軸器 23
4-2-5 深溝滾珠軸承組 24
4-2-6 齒輪組 25
4-2-7 加速規 26
4-2-8 DAQ NI 9401 27
4-2-9 電路板 28
4-2-10 DAQ NI 9234 29
4-3 實驗平台頻率響應 30
4-4 實驗類型 32
4-4-1 正常 32
4-4-2 磨損 32
4-4-3 斷齒 33
4-4-4 不平衡 33
4-5 實驗設置 34
4-6 實驗方法 34
第五章 齒輪故障診斷實驗 36
5-1 轉速設計 36
5-2 實驗一 37
5-3 實驗二 67
第六章 結論及未來展望 88
6-1 結論 88
6-2 未來展望 89
參考文獻 90
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[2]Huang, N. E. and Wu, Z., 2004, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of Royal Society London. A, No. 460, pp. 1597-1611.
[3]Han, L., 2010, “Gear fault detection and diagnosis based on Hilbert-Huang Transform,” IEEE 3rd International Congress on Image and Signal Processing, pp. 3323-3326.
[4]于德介、程軍聖、楊宇,2006,機械故障診斷的Hilbert-Huang變換方法,科學出版社。
[5]Huang, N. E. and Wu, Z., 2005, “Ensemble empirical mode decomposition: a noise assisted data analysis method,” Center for Ocean-Land-Atmosphere Studies, Technical Report series, Vol. 193, No. 173.
[6]Huang, N. E. and Wu, Z., 2009, “Ensemble empirical mode decomposition: a noise assisted data analysis method,” Advances in Adaptive Data Analysis, Vol. 1, No. 1, pp. 1–41.
[7]張智傑,2005,“齒輪故障診斷之模糊類神經網路,”中原大學機械工程學系碩士論文。
[8]Ai, S. and Li, H., 2008, “Gear fault detection based on ensemble empirical mode decomposition and Hilbert-Huang transform,” IEEE Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 173-177.
[9]Li, H., Zheng, H. and Tang, L., 2009, “Gear fault diagnosis based on order tracking and Hilbert-Huang transform,” IEEE Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 468-472.
[10]Zhang, Y., 2006, “Order bispectrum based gearbox fault diagnosis during speed-up process,” IEEE Proceedings of the 6th World Congress on Intelligent Control and Automation, pp. 5526-5529.
[11]Fyfe, K. R. and Munck, E. D. S., 1997, “Analysis of computed order tracking,” Mechanical Systems and Signal Processing, Vol. 11, No. 2, pp. 187-205.
[12]Bossley, K. M. and Mckendrick, R. J., 1999, “Hybrid computed order tracking,” Mechanical Systems and Signal Processing, Vol. 13, No. 4, pp. 627-641.
[13]Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X. and Blank, K., 2009, “On instantaneous frequency,” Advances in Adaptive Data Analysis, Vol. 1, No. 2, pp. 177-229.
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