| 研究生: |
賴家祥 Chia-hsiang Lai |
|---|---|
| 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 |
| 指導教授: |
李雄
Shyong Lee |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 畢業學年度: | 100 |
| 語文別: | 中文 |
| 論文頁數: | 184 |
| 中文關鍵詞: | 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 |
| 外文關鍵詞: | Hilbert-Hung Transform, dimensionless frequency normalization, order tracking, Support Vector Machines, fault diagnosis |
| 相關次數: | 點閱:16 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
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。
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.
[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/