Parameter optimized Morlet wavelet de-noising method based on cross validation method

Chongqing Daxue Xuebao/Journal of Chongqing University

Volumn 33, Issue 1, 2010, Pages 1-6

  Tang, Bao Ping ^a   Liu, Wen Yi ^a   Jiang, Yong Hua ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

Cross validation method; Fault diagnosis; Improved Morlet wavelet; Wavelet de noising; Wavelet transform

Indexed keywords

CROSS-VALIDATION METHODS; DE-NOSING; DENOISING METHODS; FAULT DIAGNOSIS; FAULT FEATURE; GEAR FAULT DIAGNOSIS; MECHANICAL SHOCK; MORLET WAVELET; MOTHER WAVELETS; PARAMETER-OPTIMIZED; SCALE PARAMETER; TIME DOMAIN; TWO PARAMETER; WAVELET DENOISING; WAVELET TRANSFORMATIONS;

FEATURE EXTRACTION; WAVELET TRANSFORMS;

FAILURE ANALYSIS;

EID: 77949891440     PISSN: 1000582X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Tsai N C, King Y H, Lee R M. Fault diagnosis for magnetic bearing systems[J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1339-1351.
2. Al-Raheem K F, Roy A, Ramachandran K P, et al. Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique[J]. International Journal of Advanced Manufacturing Technology, 2009, 40(3/4): 393-402.
3. Bozchalooi I S, Liang M. A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection[J]. Journal of Sound and Vibration, 2007,308 (1/2): 246-267.
4. Chanerley A A, Alexander N A. Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising[J]. Computers & Structures, 2007, 85(21/22): 1679-1692.
5. Zhang Y, Mu W, Amin M G. Subspace analysis of spatial time-frequency distribution matrices[J]. IEEE Transactions on Signal Processing, 2001, 49(4):747-758.
6. Qiu H, Lee J, Lin J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006, 289(4/5):1066-1090.
7. Lin J, Zuo M J, Fyfe K R. Mechanical fault detection based on the wavelet de-noising technique[J]. ASME Journal of Vibration and Acoustics, 2004, 126 (1):9-16.
8. Rafiee J, Tse P W, Harifi A, et al. A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system[J]. Expert Systems with Applications, 2009, 36(3): 4862-4875.
9. Wang Qiao-Lan, Ji Zhong, Qin Shu-Ren. EEG signal denoising based on wavelet transform[J]. Journal of Chongqing University: Natural Science Edition, 2005, 28(7):15-17.
10. Cheng Fa-Bin, Tang Bao-Ping, Zhong You-Ming. A de-noising method based on optimal Morlet wavelet and singular value decomposition and its application in fault diagnosis[J]. Journal of Vibration and Shock, 2008, 27(2):91-94.
11. Sailhac P, Gibert D, Boukerbout H. The theory of the continuous wavelet transform in the interpretation of potential fields: a review[J]. Geophysical Prospecting, 2009, 57(4): 517-525.
12. Saravanan N, Ramachandran K I. A case study on classification of features by fast single-shot multiclass PSVM using Morlet wavelet for fault diagnosis of spur bevel gear box[J]. Expert Systems with Applications, 2009, 36(8): 10854-10862.
13. 2007.
14. Minowa Y. Verification for generalizability and accuracy of a thinning-trees selection model with the ensemble learning algorithm and the cross-validation method[J]. Journal of Forest Research, 2008, 13(5): 275-285.
15. Wang Y, Geppert H, Bajorath J. Shannon entropy-based fingerprint similarity search strategy[J]. Journal of Chemical Information and Modeling, 2009, 49 (7): 1687-1691.