Roller bearing fault diagnosis method based on LMD and neural network

Zhendong yu Chongji/Journal of Vibration and Shock

Volumn 29, Issue 8, 2010, Pages 141-144

  Cheng, Jun Sheng ^a   Shi, Mei Li ^a   Yang, Yu ^a  

^a HUNAN UNIVERSITY   (China)

Author keywords

Fault diagnosis; Feature parameter; LMD; Neural network; Roller bearing

Indexed keywords

ANALYSIS RESULTS; DIAGNOSIS METHODS; FAULT DIAGNOSIS; FAULT PATTERNS; FEATURE PARAMETERS; FINITE NUMBER; INPUT PARAMETER; LMD; LOCAL MEAN DECOMPOSITIONS; NON-STATIONARY VIBRATION SIGNALS; ORIGINAL SIGNAL; OUTER RACES; PRODUCT FUNCTIONS; ROLLER BEARING FAULT; TIME DOMAIN; WAVELET PACKET ANALYSIS;

BEARINGS (STRUCTURAL); ROLLER BEARINGS; ROLLERS (MACHINE COMPONENTS); SIGNAL PROCESSING; TIME DOMAIN ANALYSIS;

NEURAL NETWORKS;

EID: 77957608004     PISSN: 10003835     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Baydar N, Ball A. Detection of gear failures via vibration and acoustics signals using wavelet transform [J]. Mechanical Systems and Signal Processing, 2003, 17 (4): 787-804.
2. Zheng H, Li Z, Chen X. Gear fault diagnosis based on continuous wavelet transform [J]. Mechanical Systems and Signal Processing, 2002, 16 (2-3): 447-457.
3. Classen T, Mecklenbrauker W. The aliasing problem in discrete-time Wigner distribution [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1983, 31 (5): 1067-1072.
4. Lee J H, Kim J, Kim H J. Development of enhanced Wigner-Ville distribution function [J]. Mechanical Systems and Signal Processing, 2001, 13 (2): 367-398.
5. Cohen L. Time-frequency distribution-a review [A]. Proceedings of the IEEE, 1989, 77 (7): 941-981.
6. Mallat S. A theory for multi-resolution decomposition, the wavelet representation [J]. IEEE Trans. P. A. M. I. , 1989, 11 (7): 674-689.
7. Huang N E, Shen Z, Long S R, et al. The Empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc. R. Soc. Lond. A, 1998, 454: 903-995.
8. Huang N E, Shen Z, Long S R. A New View of Nonlinear Water Waves: The Hilbert Spectrum [J]. Annu. Rev. Fluid Mech., 1999, 31: 417-457.
9. Cheng Junsheng, Yu Dejie, Yang Yu. Energy operator demodulating approach based on EMD and its application in mechanical fault diagnosis [J]. Chinese Journal of Mechanical Engineering, 2004, 40 (8): 115-118.
10. Loh C H, Wu T C, Huang N E. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural response [J]. Bulletin of the Seismological Society of American, 2001, 91 (5): 1339-1357.
11. Marcus D, Torsten S. Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves [J]. Ocean Engineering, 2004, 31 (14-15): 1783-1834.
12. Qin S R, Zhong Y M. A new algorithm of Hilbert-Huang transform [J]. Mechanical Systems and Signal Processing, 2006, 20 (8): 1941-1952.
13. Huang N E, Wu M L C, Long S R. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis [J]. Proc. R. Soc. Lond. A, 2003, 459: 2317-2345.
14. Deng Yongjun, Wang wei. Boundary processing technique in EMD method and Hilbert transform [J]. Chinese Science Bulletin, 2001, 46 (11): 257-263.
15. Cheng Junsheng, Yu Dejie, Yang Yu. Research on the intrinsic mode function (IMF) criterion in EMD method [J]. Mechanical Systems and Signal Processing, 2006, 20 (4): 817-824.
16. Smith J S. The local mean decomposition and its application to EEG perception data [J]. Journal of the Royal Society Interface, 2005, 2 (5): 443-454.