Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution

Renewable Energy

Volumn 35, Issue 12, 2010, Pages 2862-2866

  Tang, Baoping ^a   Liu, Wenyi ^a   Song, Tao ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

Continuous wavelet transformation (CWT); Fault diagnosis method; Morlet wavelet; Wavelet de noising; Wigner Ville distribution (WVD)

Indexed keywords

CONTINUOUS WAVELET TRANSFORMATION; FAULT DIAGNOSIS METHOD; MORLET WAVELET; WAVELET DENOISING; WIGNER-VILLE DISTRIBUTION;

CONDITION MONITORING; FAILURE ANALYSIS; FEATURE EXTRACTION; STEADY FLOW; VIBRATION ANALYSIS; WAVELET TRANSFORMS; WIND TURBINES;

WIND POWER;

ADSORPTION; LIGHTNING; MOISTURE; MONITORING; VIBRATION; WAVELET ANALYSIS; WIND TURBINE; WIND VELOCITY;

EID: 77954313196     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2010.05.012     Document Type: Article

References (27)

1. Liu W.Y., Tang B.P., Jiang Y.H. Status and problems of wind turbine structural health monitoring techniques in China. Renewable Energy 2010, 35(7):1414-1418.
2. Dalili N., Edrisy A., Carriveau R. A review of surface engineering issues critical to wind turbine performance. Renewable and Sustainable Energy Reviews 2009, 13:428-438.
3. Roshan-Ghias A., Shamsollahi M.B., Mobed M., Behzad M. Estimation of modal parameters using bilinear joint time-frequency distributions. Mechanical Systems and Signal Processing 2007, 21:2125-2136.
4. Padovese L.R. Hybrid time-frequency methods for non-stationary mechanical signal analysis. Mechanical Systems and Signal Processing 2004, 18:1047-1064.
5. Rodriguez M.A., Emeterio J. Ultrasonic flaw detection in NDE of highly scattering materials using wavelet and Wigner-Ville transform processing. Ultrasonics 2004, 42:217-250.
6. Gelman L., Adamenko V. Usage of the Wigner-Ville distribution for vibro-acoustcal diagnostics of cracks. Journal of the Acoustical Society of America 2000, 108. Proceedings of the Meeting of the Acoustical Society of America, [5, Part 2].
7. Khalid Al-Raheem F., Roy Asok, et al. Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique. International Journal of Advanced Manufacturing Technology 2009, 40:393-402.
8. Tsai Nan-Chyuan, King Yueh-Hsun, Lee Rong-Mao Fault diagnosis for magnetic bearing systems. Mechanical Systems and Signal Processing 2009, 23:1339-1351.
9. Lee S.K. A new method for smoothing non-oscillation cross-terms in sliced wigner fourth-order moment spectra. Mechanical Systems and Signal Processing 2001, 15:1023-1029.
10. Khandan F., Ayatollahi A. Performance region of center affine filter for liminating of interference terms of discrete Wigner distribution. Image and Signal Processing and Analysis 2003, 2:621-625.
11. Loutridis S.J. Instantaneous energy density as a feature for gear fault detection. Mechanical Systems and Signal Processing 2006, 20:1239-1253.
12. Junsheng C., Dejie Y., Yu Y. Time-energy density analysis based on wavelet transforms. NDT & E International 2005, 38:569-572.
13. Yang W.X., Ren X.M. Detecting impulses in mechanical signals by wavelets. EURASIP Journal on Applied Signal Processing 2004, 8:1156-1162.
14. Chanerley A.A., Alexander N.A. Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising. Computers & Structures 2007, 85:1679-1692.
15. Fan Y.S., Zheng G.T. Research of high-resolution vibration signal detection technique and application to mechanical fault diagnosis. Mechanical Systems and Signal Processing 2007, 21:678-687.
16. Ioannis Delakis, Omer Hammad, Kitney Richard I. Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI). Physics in Medicine and Biology 2007, 52:3741-3751.
17. Rafiee J., Tse P.W., Harifi A., Sadeghi M.H. A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system. Expert Systems with Applications 2009, 36:4862-4875.
18. Bozchalooi I.Soltani, Liang Ming A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection. Journal of Sound and Vibration 2007, 308:246-267.
19. Kumari R., Selva Shantha, Sadasivam V. De-noising and baseline wandering removal of electrocardiogram using double density discrete wavelet. International Journal of Wavelets, Multiresolution and Information Processing 2007, 3:399-415.
20. Pachori RB, Sircar P. A novel technique to reduce cross terms in the squared magnitude of the wavelet transform and the short-time Fourier transform, Proc. IEEE Int. Workshop on Intelligent Signal Processing, Faro, Portugal, 1-3 September, 2005:217-222.
21. Mirela B., Isar A. The reduction of interference terms in the time-frequency plane. Signals, Circuits and Systems 2003, 2:461-464.
22. Liu W.Y., Tang B.P., Chen R.X., Jiang Y.H. A method to suppress the cross terms in Wigner-Ville distribution using auto terms. China Mechanical Engineering 2009, 20(21):2613-2616. [in Chinese].
23. Zhu Z.K., Yan Ruqiang, Luo Liheng Detection of signal transients based on wavelet and statistics for machine fault diagnosis. Mechanical Systems and Signal Processing 2009, 23:1076-1097.
24. Lin J., Zuo M.J., Fyfe K.R. Mechanical fault detection based on the wavelet de-noising technique. ASME Journal of Vibration and Acoustics 2004, 126:9-16.
25. Tang B.P., Liu W.Y., Jiang Y.H. Parameter optimized Morlet wavelet de-noising method based on cross validation method. Journal of Chongqing University 2010, 33(1):1-6. [in Chinese].
26. Cohen I., Raz S., Malah D. Adaptive suppression of Wigner interference-terms using shift-invariant wavelet packet decompositions. Signal Processing 1999, 73:203-223.
27. Cohen L. Time-frequency distributions-a review. Proceedings of the IEEE 1989, 77:941-981.