Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis

Mechanical Systems and Signal Processing

Volumn 50-51, Issue , 2015, Pages 70-100

  Ming, A B ^a,b   Zhang, W ^b   Qin, Z Y ^a   Chu, F L ^a  

^a TSINGHUA UNIVERSITY   (China)

^b XI'AN RESEARCH INSTITUTE OF HIGH TECHNOLOGY   (China)

Author keywords

Deterministic component cancellation; Fault diagnosis; Hilbert transform; Multi component signal; Rolling element bearings

Indexed keywords

FAILURE ANALYSIS; FEATURE EXTRACTION; SIGNAL PROCESSING; BEARINGS (MACHINE PARTS); ITERATIVE METHODS; MATHEMATICAL TRANSFORMATIONS; ROLLER BEARINGS; SIGNAL RECONSTRUCTION; VIBRATION ANALYSIS;

BEARING FAULT DIAGNOSIS; DETERMINISTIC COMPONENT; HILBERT TRANSFORM; MULTICOMPONENT SIGNALS; REAL-TIME SIGNAL PROCESSING; ROLLING ELEMENT BEARING; THEORETICAL CALCULATIONS; TIME AND FREQUENCY DOMAINS;

ITERATIVE METHODS; FAULT DETECTION;

EID: 84905717973     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2014.05.033     Document Type: Article

References (37)

1. R.B. Randall, and J. Antoni Rolling element bearing diagnostics - a tutorial Mech. Syst. Signal Process. 25 2 2011 485 520
2. N. Sawalhi, R.B. Randall, and H. Endo The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis Mech. Syst. Signal Process. 21 6 2007 2616 2633
3. R. Hao, Z. Peng, Z. Feng, and F. Chu Application of support vector machine based on pattern spectrum entropy in fault diagnostics of rolling element bearings Meas. Sci. Technol. 22 4 2011 45708 45720
4. J.S. Cheng, D. Yu, and Y. Yang The application of energy operator demodulation approach based on EMD in machinery fault diagnosis Mech. Syst. Signal Process. 21 2 2007 668 677
5. H. Qiu, J. Lee, J. Lin, and G. Yu Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics J. Sound Vib. 289 4-5 2006 1066 1090
6. R. Yan, and R. Gao Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis Tribol. Int. 42 2 2009 293 302
7. R. Hao, and F. Chu Morphological un-decimated wavelet decomposition for fault diagnostics of rolling element bearings J. Sound Vib. 320 4-5 2009 1164 1177
8. Z. Peng, and F. Chu Application of the wavelet transform in machine condition monitoring and fault diagnostics a review with bibliography Mech. Syst. Signal Process. 18 2 2004 199 221
9. Z. Peng, P. Tse, and F. 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
10. Q. Du, and S. Yang Application of the EMD method in the vibration analysis of ball bearings Mech. Syst. Signal Process. 21 6 2007 2634 2644
11. Y. Lei, Z. He, and Y. Zi EEMD method and WNN for fault diagnosis of locomotive roller bearings Expert Syst. Appl. 38 6 2011 7334 7341
12. J. Antoni The spectral kurtosis a useful tool for characterising non-stationary signals Mech. Syst. Signal Process. 20 2 2006 282 307
13. 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
14. J. Antoni Fast computation of the kurtogram for the detection of transient faults Mech. Syst. Signal Process. 21 1 2007 108 124
15. D. Wang, P.W. Tse, and K.L. Tsui An enhanced kurtogram method for fault diagnosis of rolling element bearings Mech. Syst. Signal Process. 35 1-2 2013 176 199
16. T. Barszcz, and A. Jabłoński A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram Mech. Syst. Signal Process. 25 1 2011 431 451
17. P.W. Tse, and D. Wang The design of a new sparsogram for fast bearing fault diagnosis Part 1 of the two related manuscripts that have a joint title as two automatic vibration-based fault diagnostic methods using the novel sparsity measurement - part 1 and 2 Mech. Syst. Signal Process. 40 2 2013 499 519
18. P.W. Tse, and D. Wang The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection Part 2 of the two related manuscripts that have a joint title as two automatic vibration-based fault diagnostic methods using the novel sparsity measurement-part 1 and 2 Mech. Syst. Signal Process. 40 2 2013 520 544
19. R.B. Randall, J. Antoni, and S. Chobsaard The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals Mech. Syst. Signal Process. 15 5 2001 945 962
20. I. Antonidais, and G. Glossiotis Cyclostationary analysis of rolling-element bearing vibration signals J. Sound Vib. 248 5 2001 829 845
21. J. Antoni Cyclic spectral analysis in practice Mech. Syst. Signal Process. 21 2 2007 597 630
22. J. Antoni Cyclic spectral analysis of rolling-element bearing signals facts and fictions J. Sound Vib. 304 3-5 2007 497 529
23. J. Antoni Cyclostationarity by examples Mech. Syst. Signal Process. 23 4 2009 987 1036
24. J. Urbanek, J. Antoni, and T. Barszcz Detection of signal component modulations using modulation intensity distribution Mech. Syst. Signal Process. 28 0 2012 399 413
25. D. Ho, and R. Randall Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals Mech. Syst. Signal Process. 14 5 2000 763 788
26. A.B. Ming, Z.Y. Qin, W. Zhang, and F.L. Chu Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings Mech. Syst. Signal Process. 41 1-2 2013 141 154
27. P. Borghesani, P. Pennacchi, and S. Chatterton The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings Mech. Syst. Signal Process. 43 1-2 2014 25 43
28. J. Antoni, and R.B. Randall Unsupervised noise cancellation for vibration signals Part I - evaluation of adaptive algorithms Mech. Syst. Signal Process. 18 1 2004 89 101
29. S. Braun The synchronous (time domain) average revisited Mech. Syst. Signal Process. 25 4 2011 1087 1102
30. J. Antoni, and R.B. Randall Unsupervised noise cancellation for vibration signals part II - a novel frequency-domain algorithm Mech. Syst. Signal Process. 18 1 2004 103 117
31. P. Borghesani, P. Pennacchi, R. Randall, N. Sawalhi, and R. Ricci Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions Mech. Syst. Signal Process. 36 2 2013 370 384
32. R.B. Randall, N. Sawalhi, and M. Coats A comparison of methods for separation of deterministic and random signals Int. J. Cond. Monit. 1 1 2011 11 19
33. P.D. McFadden, and J.D. Smith Model for the vibration produced by a single point defect in a rolling element bearing J. Sound Vib. 96 1 1984 69 82
34. J. Antoni, and R.B. Randall A stochastic model for simulation and diagnostics of rolling element bearings with localized faults Trans. ASME, J. Vib. Acoust. 125 3 2003 282 289
35. M. Feldman Hilbert Transform Applications in Mechanical Vibration 2011 John Wiley and Sons Chichester
36. M. Feldman Hilbert transform in vibration analysis Mech. Syst. Signal Process. 25 3 2011 735 802
37. D. Bentley, Predictive Maintenance Through the Monitoring and Diagnostics of Rolling Element Bearings, Bentley Nevala Co., Nevada, Application Note 44, 1989, pp. 2-8.