A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

Mechanical Systems and Signal Processing

Volumn 81, Issue , 2016, Pages 126-147

  Grasso, M ^a   Chatterton, S ^a   Pennacchi, P ^a   Colosimo, B M ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Bearing; Combined Mode Functions; Empirical Mode Decomposition; Fault detection; Vibration

Indexed keywords

BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); FAULT DETECTION; MACHINERY; PROBABILITY DENSITY FUNCTION; ROLLER BEARINGS; ROTATING MACHINERY; SIGNAL DETECTION; SIGNAL DISTORTION; VIBRATION ANALYSIS;

EMPIRICAL MODE DECOMPOSITION; INFORMATION CONTENTS; INTRINSIC MODE FUNCTIONS; MODE FUNCTIONS; ROLLING ELEMENT BEARING; SEPARATION OF COMPONENTS; TIME FREQUENCY ANALYSIS; VIBRATION;

SIGNAL PROCESSING;

EID: 84978319097     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2016.02.067     Document Type: Article

References (39)

1. R.B. Randall, and J. Antoni Rolling element bearing diagnostics-A tutorial Mech. Syst. Signal Process. 25 2 2011 485 520
2. 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
3. F. Al-Badour, M. Sunar, and L. Cheded Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques Mech. Syst. Signal Process. 25 6 2011 2083 2101
4. A. Ayenu-Prah, and N. Attoh-Okine A Criterion for selecting relevant intrinsic mode functions in empirical mode decomposition Adv. Adapt. Data Anal. 2 1 2010 1 24
5. P. Flandrin,. Goncalves G., Rilling detrending and denoising with empirical mode decomposition, in: Proceedings of the XII European Signal Processing Conference (EUSIPCO), Vienna, Austria, September, 2004, pp. 6-10
6. Q. Gao, C. Duan, H. Fan, and Q. Meng Rotating machine fault diagnosis using empirical mode decomposition Mech. Syst. Signal Process. 22 5 2008 1072 1081
7. N.E. Huang, Z. Shen, S.R. Long, M.L. Wu, H.H. Shih, and et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis Proc. R. Soc. Lond. Ser. A 454 1998 903 995
8. V.K. Rai, and A.R. Mohanty Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform Mech. Syst. Signal Process. 21 6 2007 2607 2615
9. Z. Wu, and N.E. Huang Ensemble empirical mode decomposition: A noise assisted data analysis method Adv. Adapt. Data Anal. 1 1 2009 1 41
10. H. Liang, Q.-H. Lin, and J.D.Z. Chen Application of the empirical mode decomposition to the analysis of the esophageal manometric data in gastroesophageal reflux disease IEEE Trans. Biomed. Eng. 52 10 2005 1692 1701
11. N. Bu, N. Ueno, O. Fukuda, Monitoring of Respiration and Heartbeat during Sleep using a flexible piezoelectric film sensor and empirical mode decomposition, in: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, 2007.
12. R. Ricci, and P. Pennacchi Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions Mech. Syst. Signal Process. 25 3 2011 821 838
13. M. Grasso, P. Pennacchi, and B.M. Colosimo Empirical mode decomposition of pressure signal for health condition monitoring in waterjet cutting Int. J. Adv. Manuf. Technol. 72 1-4 2014 347 364
14. Z. Peng, F. Chu, and Y. He Vibration signal analysis and feature extraction based on reassigned wavelet scalogram J. Sound Vib. 253 5 2002 1087 1100
15. Z.K. Peng, W.T. Peter, 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 5 2005 974 988
16. W. Guo, and P.W. Tse A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals J. Sound Vib. 332 2 2013 423 441
17. R. Yan, and R.X. Gao Rotary machine health diagnosis based on empirical mode decomposition J. Vib. Acoust. 130 2 2008 2007
18. C. Junsheng, Y. Dejie, 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
19. Y. Lei, J. Lin, Z. He, and M.J. Zuo A review on empirical mode decomposition in fault diagnosis of rotating machinery Mech. Syst. Signal Process. 35 1 2013 108 126
20. J. Zhang, R. Yan, R.X. Gao, and Z. Feng Performance enhancement of ensemble empirical mode decomposition Mech. Syst. Signal Process. 24 2010 2104 2123
21. A.J. Izenman Review papers: recent developments in nonparametric density estimation J. Am. Stat. Assoc. 86 413 1991 205 224
22. A.W. Bowman, and A. Azzalini Applied Smoothing Techniques for Data Analysis the Kernel Approach with S-Plus Illustrations 1997 Oxford University Press Oxford
23. S.J. Sheather Density estimation Stat. Sci. 9 4 2004 588 597
24. A.Z. Zambon, R. Dias, A review of Kernel Density Estimation with Applications to Econometrics, arXiv:1212.2812v1 [stat.ME], 2012, pp. 1-35.
25. A.W. Bowman An alternative method of cross-validation for the smoothing of kernel density estimates Biometrika 71 1984 353 360
26. W. Hardle, J.S. Marron, and M.P. Wand Bandwidth choice for density derivatives J. R. Stat. Soc., Ser. B (Methodological) 1990 223 232
27. M.C. Jones, and R.F. Kappenman On a class of kernel density estimate bandwidth selectors Scand. J. Stat. 1992 337 349
28. M.C. Jones, J.S. Marron, and S.J. Sheather A brief survey of bandwidth selection for density estimation J. Am. Stat. Assoc. 91 433 1996 401 407
29. A.C. Guidoum, Kedd: Kernel Estimator and Bandwidth Selection for Density and its Derivatives. R package version 1.0.3, 2015. (http://CRAN.R-project.org/package=kedd)
30. L. Vendramin, R.J. Campello, and E.R. Hruschka Relative clustering validity criteria: A comparative overview, statistical analysis and data mining ASA Data Sci. J. 3 4 2010 209 235
31. G.H. Ball, D.J. Hall, ISODATA, A Novel Method of Data Analysis and Pattern Classification, Tech. Rep. NTIS No. AD 699616 Standford Research Institute, Menlo Park, 1965
32. T. Calinski, and J. Harabasz A dendrite method for cluster analysis Commun. Stat. 3 1974 1 27
33. J.A. Hartigan Clustering Algorithms 1975 Wiley New York
34. Q. Zhao, M. Xu, P. Fränti, Sum-of-squares based cluster validity index and significance analysis. Adaptive and natural computing algorithms, in: Lecture Notes in Computer Science, vol. 5495, Springer Berlin Heidelberg, 2009, pp. 313-322
35. L. Xu Bayesian Ying-Yang machine, clustering and number of clusters Pattern Recognit. Lett. 18 1997 1167 1178
36. M. Grasso, and B.M. Colosimo An automated approach to enhance multi-scale signal monitoring of manufacturing processes J. Manuf. Sci. Eng. 138 5 2016 051003-051003-16
37. G. Rilling, P. Flandrin, P. Goncalves, On empirical mode decomposition and its algorithms, in: Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (Italy), 2003
38. S.J. Loutridis Damage detection in gear systems using empirical model decomposition Eng. Struct. 26 12 2004 1833 1841
39. W.N. Venables, and B.D. Ripley Modern Applied Statistics with S 2002 Springer New York