Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

Mechanical Systems and Signal Processing

Volumn 60, Issue , 2015, Pages 273-288

  Liu, Hongmei ^a   Wang, Xuan ^b   Lu, Chen ^a,b  

^a BEIHANG UNIVERSITY   (China)

^b SCIENCE AND TECHNOLOGY ON RELIABILITY AND ENVIRONMENTAL ENGINEERING LABORATORY   (China)

Author keywords

Bearing fault diagnosis; Local characteristic scale decomposition (LCD); Multifractal detrended fluctuation analysis (MFDFA); Variable working conditions

Indexed keywords

FAILURE ANALYSIS; FAULT DETECTION; FRACTALS; INSTRUMENT SCALES; MATHEMATICAL TRANSFORMATIONS; SIGNAL PROCESSING;

BEARING FAULT DIAGNOSIS; GENERALIZED HURST EXPONENT; HILBERT HUANG TRANSFORMS; LOCAL CHARACTERISTIC-SCALE DECOMPOSITIONS; MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS; PRINCIPAL COMPONENTS ANALYSIS; ROLLING BEARING VIBRATION; VARIABLE WORKING CONDITIONS;

ROLLER BEARINGS;

EID: 84925960336     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.02.002     Document Type: Article

References (27)

1. M.N. Khajavi, and M.N. Keshtan Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform J. Vibroeng. 16 2 2014 761 769
2. B. Wang, S.L. Liu, H.L. Zhang, and C. Jiang Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis J. Vibroeng. 16 1 2014 57 69
3. B. Sreejith, A. Verma, A. Srividya, Fault diagnosis of rolling element bearing using time-domain features and neural networks, in: Proceedings of the IEEE Region 10 and the Third International Conference on Industrial and Information Systems ICIIS 2008, 2008, pp. 1-6.
4. G.X. Zhang, and J.J. Zhu A new time domain signal analysis technique for early fault diagnosis of rolling element bearing J. Jiangxi Univ. Sci. Technol. 2 2009 469 480
5. Y.F. Wang, P.J. Kootsookos, Frequency estimation in the fault detection of rolling element bearing, in: Proceedings of the Fifth International Congress On Sound and Vibration, 1997, pp. 1073-1080.
6. J.K. Zhou, and S.L. Lu Fault diagnosis of rolling bearing based on emd average energy method Light Ind. Mach. 2 2010 269 277
7. Z. Feng, M. Liang, and F. Chu Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples Mech. Syst. Signal Process. 38 2013 165 205
8. R.R. Coifman, and M.V. Wickerhauser Entropy-based algorithms for best basis selection IEEE Trans. Inf. Theory 38 1992 713 718
9. Z. Wu, and N.E. Huang Ensemble empirical mode decomposition: a noise-assisted data analysis method Adv. Adapt. Data Anal. 1 2009 1 41
10. Y. Wang, Z. He, and Y. Zi A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis Meas. Sci. Technol. 20 2009 025704
11. J. Cheng, K. Zhang, and Y. Yang Ensemble local mean decomposition method based on noise-assisted analysis Jixie Gongcheng Xuebao (Chin. J. Mech. Eng.) 47 2011 55 62
12. J. Zheng, J. Cheng, and Y. Yang A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy Mech. Mach. Theory 70 2013 441 453
13. P. Li, Q. He, F. Kong, An approach for fault diagnosis of bearings using wavelet-based fractal analysis, in: Proceddings of the IEEE International Conference on Information and Automation (ICIA), 2010, pp. 2338-2343.
14. Q.H. Wang, and X.B. Zhang Application of fractal theory to fault diagnosis for hydraulic pump J. Dalian Marit. Univ. 2 2004 012
15. Y. Yu, L. Baoliang, S. Jingshan, Y. Shixuan, The application of vibration signal multi-fractal in fault diagnosis, in: Proceedings of the IEEESecond International Conference on Future Networks, 2010, pp. 164-167.
16. P.Z. Ke, H.Y. Yong, L. Qing, and C. Fulei Wavelet multifractal spectrum: application to analysis vibration signals Chin. J. Mech. Eng. 8 2002 013
17. J.W. Kantelhardt, S.A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H.E. Stanley Multifractal detrended fluctuation analysis of nonstationary time series Phys. A: Stat. Mech. Appl. 316 2002 87 114
18. T.J. Wang, Z.P. Feng, R.J. Hao, and F.L. Chu Fault diagnosis of rolling element bearings based on teager energy operator J. Vib. Shock 31 2012 1 5
19. G. Rilling, P. Flandrin, P. Goncalves, On empirical mode decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP, 2003, pp. 8-11.
20. Y. Deng, W. Wang, C. Qian, Z. Wang, and D. Dai Boundary-processing-technique in EMD method and Hilbert transform Chin. Sci. Bull. 46 2001 954 960
21. H. Li, Y. Zhang, and H. Zheng Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings J. Mech. Sci. Technol. 23 2009 291 301
22. M. Le Van Quyen, J. Foucher, J.P. Lachaux, E. Rodriguez, A. Lutz, J. Martinerie, and F.J. Varela Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony J. Neurosci. Methods 111 2001 83 98
23. H. Li, H. Zheng, and L. Tang Gear fault detection based on teager-huang transform Int. J. Rotat. Mach. 2010) 2010
24. P. Shang, Y. Lu, and S. Kamae Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis Chaos, Solitons Fractals 36 2008 82 90
25. D. Yu, J. Cheng, and Y. Yang Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings Mech. Syst. Signal Process. 19 2005 259 270
26. R. Dunia, and S. Joe Qin Joint diagnosis of process and sensor faults using principal component analysis Control Eng. Pract. 6 1998 457 469
27. E. De Moura, C. Souto, A. Silva, and M. Irmao Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses Mech. Syst. Signal Process. 25 2011 1765 1772