Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment

Mechanical Systems and Signal Processing

Volumn 72-73, Issue , 2016, Pages 160-183

  Pan, Jun ^a   Chen, Jinglong ^a   Zi, Yanyang ^a   Li, Yueming ^b   He, Zhengjia ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Data driven adaptive Fourier spectrum segment; Empirical wavelet transform; Feature extraction; Mechanical fault diagnosis

Indexed keywords

CONDITION MONITORING; EXTRACTION; FAILURE ANALYSIS; FEATURE EXTRACTION; FOURIER TRANSFORMS; MODULATION; SIGNAL PROCESSING; VIBRATIONS (MECHANICAL); WAVELET TRANSFORMS;

CONDITION-MONITORING DATA; ENVELOPE SPECTRA; FOURIER SPECTRA; GAUSSIAN FUNCTIONS; MECHANICAL FAULT DIAGNOSIS; MECHANICAL FAULTS; MODULATION INFORMATION; PARTITION SCHEMES;

FAULT DETECTION;

EID: 84955487882     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.10.017     Document Type: Article

References (45)

1. Q. Hai, L. Jay, L. Jing, and et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics J. Sound Vib. 289 4 2006 1066 1090
2. Z.K. Zhu, R.Q. Yan, L.H. Luo, and et al. Detection of signal transients based on wavelet and statistics for machine fault diagnosis Mech. Syst. Signal Process. 23 4 2009 1076 1097
3. M. Yang, J. Chen, and G.M. Dong Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum Mech. Syst. Signal Process. 25 5 2011 1773 1785
4. R.B. Randall, and J. Antoni Rolling element bearing diagnostics - a tutorial Mech. Syst. Signal Process. 25 2 2011 485 520
5. S.L. Hahn, Hilbert transforms in signal processing, Artech House on Demand, 1996.
6. P. Maragos, J.F. Kaiser, and T.F. Quatieri Energy separation in signal modulations with application to speech analysis IEEE Trans. Signal Process. 41 10 1993 3024 3051
7. P. Maragos, J.F. Kaiser, and T.F. Quatieri On amplitude and frequency demodulation using energy operators IEEE Trans. Signal Process. 41 4 1993 1532 1550
8. M. Feldman Hilbert transform in vibration analysis Mech. Syst. Signal Process. 25 3 2011 735 802
9. B.J. Chen, Z.J. He, X.F. Chen, and et al. A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis Meas. Sci. Technol. 22 5 2011 055704
10. J.S. Cheng, D.J. Yu, and Y. Yu The application of energy operator demodulation approach based on EMD in machinery fault diagnosis Mech. Syst. Signal Process. 21 2 2007 668 677
11. H. Liu, X. Wang, and C. Lu Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis Mech. Syst. Signal Process. 60 2015 273 288
12. Q. Yi, S.R. Qin, and Y.F. Mao Research on iterated Hilbert transform and its application in mechanical fault diagnosis Mech. Syst. Signal Process. 22 8 2008 1967 1980
13. W.S. Su, F. Wang, H. Zhu, and et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement Mech. Syst. Signal Process. 24 5 2010 1458 1472
14. S. Olhede, and A.T. Walden A generalized demodulation approach to time-frequency projections for multicomponent signals Proc. R. Soc. A: Math. Phys. Eng. Sci. 461 2059 2005 2159 2179
15. S.G. Mallat A theory for multiresolution signal decomposition: the wavelet representation IEEE Trans. Pattern Anal. Mach. Intell. 11 1989 674 693
16. J. Yuan, Z.J. He, Y.Y. Zi, and et al. Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis Mech. Syst. Signal Process 23 5 2009 1490 1508
17. B.M. Ebrahimi, J. Faiz, S. Lotfi-Fard, and et al. Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform Mech. Syst. Signal Process. 30 2012 131 145
18. R.Q. Yan, R.X. Gao, and X.F. Chen Wavelets for fault diagnosis of rotary machines: a review with applications Signal Process. 96 2014 1 15
19. A. Djebala, N. Ouelaa, and N. Hamzaoui Detection of rolling bearing defects using discrete wavelet analysis Meccanica 43 3 2008 339 348
20. J.S. Kwak Application of wavelet transform technique to detect tool failure in turning operations Int. J. Adv. Manuf. Technol. 28 2006 1078 1083
21. R.R. Coifman, Y. Meyer, S. Quake, and M.V. Wickerhauser Signal processing and compression with wavelet packets Wavelets Appl 442 1994 363 379
22. G.G. Yen, and K.C. Lin Wavelet packet feature extraction for vibration monitoring IEEE Trans. Ind. Electron. 47 3 2000 650 667
23. X.F. Fan, and M.J. Zuo Gearbox fault detection using Hilbert and wavelet packet transform Mech. Syst. Signal Process. 20 4 2006 966 982
24. N.E. Huang, Z. Shen, S.R. Long, and et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 454 1971 1998
25. Y. Qin Multicomponent AM-FM demodulation based on energy separation and adaptive filtering Mech. Syst. Signal Process. 38 2 2013 440 459
26. Y.G. Lei, J. Lin, Z.J. He, and et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery Mech. Syst. Signal Process. 35 1 2013 108 126
27. S.J. Loutridis Damage detection in gear systems using empirical mode decomposition Eng. Struct. 26 12 2004 1833 1841
28. H.M. Liu, X. Wang, and C. Lu Rolling bearing fault diagnosis under variable conditions using Hilbert-Huang transform and singular value decomposition Math. Probl. Eng. 765621 2014 1 10
29. Z.K. Peng, P.W. Tse, and F.L. Chu A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing Mech. Syst. Signal Process. 19 2005 974 988
30. P. Flandrin, G. Rilling, and P. Goncalves Empirical mode decomposition as a filter bank IEEE Signal Process. Lett. 11 2 2004 112 114
31. S. Olhede, and A.T. Walden The Hilbert spectrum via wavelet projections Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 460 2044 2004 955 975
32. J. Gilles Empirical wavelet transform IEEE Trans. Signal Process. 61 16 2013 3999 4010
33. J. Gilles, and K. Heal A parameterless scale-space approach to find meaningful modes in histograms - application to image and spectrum segmentation Int. J. Wavelets Multiresolution Inf. Process. 12 2014 06
34. E. Bedrosian A product theorem for Hilbert transforms Proc. IEEE 51 5 1963 868 869
35. T. Lindeberg Scale-Space Theory in Computer Vision 1993 Springer Science & Business Media
36. I. Daubechies Ten Lectures on Wavelets Vol. 61 1992 Society for industrial and applied mathematics Philadelphia
37. T. Lindeberg Scale-space for discrete signals IEEE Trans. Pattern Anal. Mach. Intell. 12 3 1990 234 254
38. J. Delon, A. Desolneux, J.L. Lisani, A.B. Petro, and A. Nonparametric Approach for histogram segmentation IEEE Trans. Image Process. 16 1 2007 253 261
39. J. Lee Rodgers, and W.A. Nicewander Thirteen ways to look at the correlation coefficient Am. Stat. 42 1 1988 59 66
40. G. Rilling, P. Flandrin, P. Goncalves, On empirical mode decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, Vol. 3, NSIP-03, Grado (I), 2003.
41. Z.H. Wu, and N.E. Huang Ensemble empirical mode decomposition: a noise-assisted data analysis method Adv. Adapt. Data Anal. 1 01 2009 1 41
42. J. Antoni, and R.B. Randall The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines Mech. Syst. Signal Process. 20 2 2006 308 331
43. J. Gilles, G. Tran, and S. Osher 2D empirical transforms. Wavelets, ridgelets, and curvelets revisited SIAM J. Imaging Sci. 7 1 2014 157 186
44. Y.G. Lei, Z.J. He, and Y.Y. Zi Application of the EEMD method to rotor fault diagnosis of rotating machinery Mech. Syst. Signal Process. 23 4 2009 1327 1338
45. Y.X. Wang, J.W. Xiang, R. Markert, and M. Liang Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications Mech. Syst. Signal Process. 67 2016 679 698