運用希爾伯特-黃轉換及無因次單位瞬時頻率正規化技術之特徵擷取於非固定轉速之軸承故障診斷

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姓名

賴家祥(Chia-hsiang Lai) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

運用希爾伯特-黃轉換及無因次單位瞬時頻率正規化技術之特徵擷取於非固定轉速之軸承故障診斷
(Fault Diagnosis of Roller Bearing under Variable Rotation Speed via Hilbert-Huang Transform and Dimensionless Instantaneous Frequency Normalization Techniques)

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摘要(中)

本研究主要針對轉動機械的軸承元件於變轉速下，軸承發生內圈損壞、滾子損壞、外圈損壞等情形之故障診斷方法探討。利用一般等時間取樣訊號量測及階次追蹤等角度訊號量測兩種方式擷取軸承振動訊號，前者的分析為提取振動訊號之包絡線，並透過希爾伯特-黃轉換結合無因次單位頻率正規化進行分析；後者的分析為提取振動訊號之包絡線，直接透過希爾伯特-黃轉換進行分析，於希爾伯特時頻譜與邊際譜探討軸承不同的損壞特徵。最後，提取邊際譜上軸承特徵頻率之幅值作為分類的特徵，並以支持向量機分類進行軸承的故障診斷，結果顯示有相當高的準確率。

摘要(英)

The main purpose of this paper is to characterize the faulted features of roller bearings, such as inner race, rolling element, and outer race with defect, under variable rotation speeds. There are two different ways to measure the vibration signals of bearings. One is the general vibration measurement with fixed sampling rate. The other one is based on the order tracking technique with identical angle increment. The envelope signals of the fixed sampling rate data are analyzed through Hilbert-Hung Transform and dimensionless frequency normalization. On the other hand, the envelope signals of the identical angle-increment data are analyzed by Hilbert-Hung Transform approach. The extracted features of the faulted bearings are discussed by observing the Hilbert time-frequency spectra as well as the marginal Hilbert spectra. With the magnitude of characteristic frequencies on the marginal Hilbert spectra as the extracted features, the classification results of supper vector machines demonstrate the high accuracy of bearing fault diagnosis.

關鍵字(中)

★ 支持向量機
★ 無因次單位頻率正規化
★ 希爾伯特-黃轉換
★ 階次追蹤
★ 故障診斷

關鍵字(英)

★ Hilbert-Hung Transform
★ dimensionless frequency normalization
★ order tracking
★ Support Vector Machines
★ fault diagnosis

論文目次

摘要 ......I
Abstract ......II
致謝 ......III
目錄 ......IV
圖目錄 ......VIII
表目錄 ......XXVII
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) ......8
2-2-1 固有模態函數(Intrinsic Mode Function, IMF) ......8
2-2-2 經驗模態分解法分解過程 ......8
2-3 瞬時頻率(Instantaneous Frequency) ......11
2-4 無因次單位瞬時頻率正規化(Dimensionless Instantaneous Frequency Normalization) ......14
2-5 希爾伯特時頻譜 (Hilbert Spectrum) 和邊際希爾伯特頻譜 (Marginal Hilbert Spectrum) ......15
2-6 支持向量機(Support Vector Machines, SVM) ......16
2-6-1 超平面(Hyper-plan) ......16
2-6-2 支持向量(Support Vector) ......16
2-6-3 核心函數(Kernel Function) ......21
2-6-4 多分類支持向量機 ......22
2-7 軸承特徵頻率 ......27
2-7-1 軸旋轉頻率(Fr) ......29
2-7-2 保持架旋轉頻率(Fc) ......29
2-7-3 滾子通過外環某點頻率(Fbpo) ......30
2-7-4 滾子通過內環某點頻率(Fbpi) ......30
2-7-5 滾子自旋頻率(Fbs) ......31
2-7-6 滾子某點通過內環及外環頻率(Frp) ......31
2-8 包絡譜分析 ......33
第三章實驗架構與實驗方法 ......34
3-1 實驗架構說明 ......34
3-2 實驗設備與規格 ......37
3-2-1 變頻器及轉速感測器 ......37
3-2-2 馬達 ......37
3-2-3 Keyphasor ......38
3-2-4 加速規 ......38
3-2-5 DIO NI 9401 ......39
3-2-6 電路板 ......40
3-2-7 DAQ NI 9234 ......41
3-2-8 DIO NI 9402 ......42
3-2-9 DAQ NI 9215 ......42
3-2-10 Encoder 及頻率轉換器 ......43
3-2-11 放大器 ......44
3-3 實驗之軸承損壞類型 ......45
3-3-1 正常軸承 ......46
3-3-2 內圈損壞軸承 ......46
3-3-3 滾柱損壞軸承 ......47
3-3-4 外圈損壞軸承 ......47
3-4 實驗設置 ......48
第四章結果與討論 ......50
4-1 等時間量測訊號分析 ......50
4-1-1 正常軸承 ......53
4-1-2 內圈損壞軸承 ......66
4-1-3 滾柱損壞軸承 ......79
4-1-4 外圈損壞軸承 ......92
4-2 等角度量測訊號分析 ......105
4-2-1 正常軸承 ......105
4-2-2 內圈損壞軸承 ......114
4-2-3 滾柱損壞軸承 ......122
4-2-4 外圈損壞軸承 ......130
4-3 特徵提取及支持向量機分類 ......138
4-3-1 特徵提取 ......138
4-3-2 支持向量機分類 ......140
第五章結論與未來展望 ......149
5-1 結論 ......149
5-2 未來展望 ......151
參考文獻 ......152

參考文獻

[1] N. E. Huang, Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, vol. 454, pp. 903-995, 1998.
[2] Z. H. Wu and N. E. Huang, "A study of the characteristics of white noise using the empirical mode decomposition method," Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, vol. 460, pp. 1597-1611, 2004.
[3] Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise assisted data analysis method," Center for Ocean-Land-Atmosphere Studies, Technical Report series, vol. 193, 2005.
[4] Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, pp. 1-41, 2009.
[5] N. Tandon and A. Choudhury, "A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings," Tribology International, vol. 32, pp. 469-480, 1999.
[6] Z. Kiral and H. Karagulle, "Simulation and analysis of vibration signals generated by rolling element bearing with defects," Tribology International, vol. 36, pp. 667-678, 2003.
[7] R. B. Randall and J. Antoni, "Rolling element bearing diagnostics-A tutorial," Mechanical Systems and Signal Processing, vol. 25, pp. 485-520, 2011.
[8] 于德介, 程軍聖, 楊宇, "機械故障診斷的Hilbert-Huang變換方法," 科學出版社, 2006.
[9] Z. K. Peng, P. W. Tse, and F. L. Chu, "A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing," Mechanical Systems and Signal Processing, vol. 19, pp. 974-988, 2005.
[10] R. Yan and R. X. Gao, "Hilbert-Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 55, pp. 2320-2329, 2006.
[11] K. R. Fyfe and E. D. S. Munck, "Analysis of computed order tracking," Mechanical Systems and Signal Processing, vol. 11, pp. 187-205, 1997.
[12] H. Li, H. Zheng, and L. Tang, "Gear fault diagnosis based on order tracking and Hilbert-Huang transform," IEEE Sixth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 4, pp. 468-472, 2009.
[13] K. M. Bossley, R. J. Mckendrick, C. J. Harris, and C. Mercer, "Hybrid computed order tracking," Mechanical Systems and Signal Processing, vol. 13, pp. 627-641, 1999.
[14] G. Madzarov, D. Gjorgjevikj, and I. Chorbev, "A Multi-class SVM Classifier Utilizing Binary Decision Tree," Informatica, vol. 33, pp. 233-241, 2009.
[15] Y. Yang, D. J. Yu, and J. S. Cheng, "A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM," Measurement, vol. 40, pp. 943-950, 2007.
[16] O. R. Seryasat, M. A. shoorehdeli, F. Honarvar, A. Rahmani, and J. Haddadnia, "Multi-fault diagnosis of ball bearing using intrinsic mode functions, Hilbert marginal spectrum and multi-class support vector machine," International Conference on Mechanical and Electronics Engineering, vol. 2, pp. 145-149, 2010.
[17] L. Cohe, "What is a Multicomponent Signal?," Acoustics, Speech, and Signal Processing, vol. 5, pp. 113-116, 1992.
[18] C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, pp. 273-297, 1995.
[19] T. H. Kjeldsen, "A contextualized historical analysis of the Kuhn-Tucker theorem in nonlinear programming: The impact of World War II," Historia Mathematica, vol. 27, pp. 331-361, 2000.
[20] 劉志剛, 李德仁, 秦前清, 史文中, "支持向量機在多分類問題中的推廣," 計算機工程與應用, pp. 10-13, 2004.
[21] 張苗, 張德賢, "多類支持向量機文本分類方法," 計算機技術與發展, vol. 18, 2008.
[22] C. C. Chang and C. J. Lin. LIBSVM -- A Library for Support Vector Machines. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm/

指導教授

李雄(Shyong Lee)

審核日期

2012-7-4

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