EEMD method and WNN for fault diagnosis of locomotive roller bearings

Expert Systems with Applications

Volumn 38, Issue 6, 2011, Pages 7334-7341

  Lei, Yaguo ^a   He, Zhengjia ^a   Zi, Yanyang ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Bearing fault diagnosis; Ensemble empirical mode decomposition; Intrinsic mode function; Wavelet neural network

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BEARING FAULT DIAGNOSIS; EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENVELOPE SPECTRA; FAULT DIAGNOSIS; FAULT DIAGNOSIS METHOD; FREQUENCY DOMAINS; FREQUENCY SPECTRA; HEALTH CONDITION; INTRINSIC MODE FUNCTION; INTRINSIC MODE FUNCTIONS; SINGLE FAULT; VIBRATION SIGNAL; WAVELET NEURAL NETWORK; WAVELET NEURAL NETWORKS;

BEARINGS (STRUCTURAL); LOCOMOTIVES; ROLLER BEARINGS; ROLLERS (MACHINE COMPONENTS); SIGNAL PROCESSING; SPECTROSCOPY; WAVELET DECOMPOSITION;

NEURAL NETWORKS;

EID: 79951581707     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.12.095     Document Type: Article

References (24)

1. U. Benko, J. Petrovčič, D. Juričić, J. Tavčar, and J. Rejec An approach to fault diagnosis of vacuum cleaner motors based on sound analysis Mechanical Systems and Signal Processing 19 2005 427 445
2. Y. Chen, B. Yang, and J. Dong Time-series prediction using a local linear wavelet neural network Neurocomputing 69 2006 449 465 (Pubitemid 43009121)
3. J.S. Cheng, D.J. Yu, and Y. Yang The application of energy operator demodulation approach based on EMD in machinery fault diagnosis Mechanical Systems and Signal Processing 21 2007 668 677
4. Flandrin, P.; Gonalvs, P.; Rilling, G. (2005). EMD equivalent filter banks, from interpretation to applications. In N.E. Huang & S.S.P. Shen (Eds.), Hilbert-Huang transform: Introduction and applications (pp. 67-87, 360pp.). Singapore: World Scientific.
5. P. Flandrin, G. Rilling, and P. Gonalvs Empirical mode decomposition as a filter bank IEEE Signal Processing Letter 11 2004 112 114
6. S. Ghohizadeh, E. Salajegheh, and P. Torkzadeh Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network Journal of Sound and Vibration 312 2008 316 331 (Pubitemid 351240930)
7. N.E. Huang, Z. Shen, and S.R. Long The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proceedings of the Royal Society of London 454 1998 903 995 (Pubitemid 128733183)
8. Y.G. Lei, Z.J. He, and Y.Y. Zi Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs Mechanical Systems and Signal Processing 21 2007 2280 2294
9. Y.G. Lei, Z.J. He, Y.Y. Zi, and Q. Hu Application of a novel hybrid intelligent method to fault diagnosis of locomotive roller bearings Transactions of the ASME: Journal of Vibration and Acoustics 130 2008 0345011 0345016
10. Y.G. Lei, Z.J. He, Y.Y. Zi, and Q. Hu Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm International Journal of Advanced Manufacturing Technology 35 2008 968 977
11. Y.G. Lei, Z.J. He, and Y.Y. Zi A new approach to intelligent fault diagnosis of rotating machinery Expert Systems with Applications 35 2008 1593 1600
12. Y.G. Lei, Z.J. He, and Y.Y. Zi New clustering algorithm-based fault diagnosis using compensation distance evaluation technique Mechanical Systems and Signal Processing 22 2008 419 435
13. Y.G. Lei, Z.J. He, and Y.Y. Zi Application of the EEMD method to rotor fault diagnosis of rotating machinery Mechanical Systems and Signal Processing 2008 10.1016/j.ymssp.2008.11.005
14. F.J. Lin, P.H. Shen, and Y.S. Kung Adaptive wavelet neural network control for linear synchronous motor servo drive IEEE Transactions on Magnetics 41 2005 4401 4412
15. L. Qu, and Z. He Mechanical diagnostics 1986 Shanghai Science and Technology Press PR China
16. V.K. Rai, and A.R. Mohanty Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform Mechanical Systems and Signal Processing 21 2007 2607 2615 (Pubitemid 46829754)
17. B. Samanta Gear fault detection using artificial neural networks and support vector machines with genetic algorithms Mechanical Systems and Signal Processing 18 2004 625 644
18. A. Subasi, A. Alkan, E. Koklukaya, and M.K. Kiymika Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing Neural Networks 18 2005 985 997 (Pubitemid 41253513)
19. M.L.D. Wong, L.B. Jack, and A.K. Nandi Modified self-organising map for automated novelty detection applied to vibration signal monitoring Mechanical Systems and Signal Processing 20 2006 593 610 (Pubitemid 41766673)
20. 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 460A 2004 1597 1611 (Pubitemid 38703409)
21. Z.H. Wu, and N.E. Huang Ensemble empirical mode decomposition: A noise assisted data analysis method Advances in Adaptive Data Analysis 1 2009 1 41
22. J.D. Wu, and C.H. Liu Investigation of engine fault diagnosis using discrete wavelet transform and neural network Expert Systems with Applications 35 3 2008 1200 1213
23. J. Zarei, and J. Poshtan An advanced Park's vectors approach for bearing fault detection Tribology International 42 2009 213 219
24. Q.H. Zhang, and A. Benveniste Wavelet networks IEEE Transactions on Neural Networks 3 1992 889 898 (Pubitemid 23580843)