Multiscale envelope manifold for enhanced fault diagnosis of rotating machines

Mechanical Systems and Signal Processing

Volumn 52-53, Issue 1, 2015, Pages 376-392

  Wang, Jun ^a   He, Qingbo ^a   Kong, Fanrang ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Continuous wavelet transform; Machinery fault diagnosis; Manifold learning; Multiscale envelope manifold; Wavelet envelope

Indexed keywords

FAILURE ANALYSIS; FAULT DETECTION; LEARNING ALGORITHMS; ROTATING MACHINERY; SIGNAL PROCESSING; MACHINERY; WAVELET ANALYSIS;

CONTINUOUS WAVELET TRANSFORM; MACHINERY FAULT DIAGNOSIS; MANIFOLD LEARNING; MULTISCALE ENVELOPE MANIFOLD; WAVELET ENVELOPE;

WAVELET TRANSFORMS;

EID: 84922918344     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2014.07.021     Document Type: Article

References (34)

1. D.L. Donoho, Denoising by soft-thresholding, IEEE Trans. Inf. Theory 41 (3) (1995) 613-627.
2. J. Lin, Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, J. Sound Vibr. 234 (2000) 135-148.
3. J. Lin, M.J. Zuo, Gearbox fault diagnosis using adaptive wavelet filter, Mech. Syst. Signal Process. 17 (2003) 1259-1269.
4. H. Qiu, J. Lee, J. Lin, G. Yu, Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, J. Sound Vibr. 289 (2006) 1066-1090.
5. P.D. Mcfadden, J.D. Smith, Model for the vibration produced by a single point defect in a rolling element bearing, J. Sound Vibr. 96 (1984) 69-82.
6. C.C. Liu, Z.D. Qiu, A method based Morlet wavelet for extracting vibration signal envelope, In: IEEE Proceedings of the Fifth International Conference on Signal Processing 1, 2000, pp. 337-340.
7. C.T. Yiakopoulos, I.A. Antoniadis, Wavelet based demodulation of vibration signals generated by defects in rolling element bearings, Shock Vibr. 9 (2002) 293-306.
8. N.G. Nikolaou, I.A. Antoniadis, Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets, Mech. Syst. Signal Process. 16 (2002) 677-694.
9. Y.T. Sheen, C.K. Hung, Construction a wavelet-based envelope function for vibration signal analysis, Mech. Syst. Signal Process. 18 (2004) 119-126.
10. R. Yan, R.X. Gao, Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis, Tribol. Int. 42 (2009) 293-302.
11. I.S. Bozchalooi, M. Liang, A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals, Mech. Syst. Signal Process. 22 (4) (2008) 915-933.
12. W. He, Z.-N. Jiang, K. Feng, Bearing fault detection based on optimal wavelet filter and sparse code shrinkage, Measurement 42 (7) (2009) 1092-1102.
13. W. Su, F. Wang, H. Zhu, Z. Zhang, Z. Guo, Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, Mech. Syst. Signal Process. 24 (5) (2010) 1458-1472.
14. Q. He, Y. Liu, Q. Long, J. Wang, Time-frequency manifold as a signature for machine health diagnosis, IEEE Trans. Instrum. Meas. 61 (5) (2012) 1218-1230.
15. J. Wang, Q. He, F. Kong, Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis, Mech. Syst. Signal Process. 40 (1) (2013) 237-256.
16. Q. He, X. Wang, Time-frequency manifold correlation matching for periodic fault identification in rotating machines, J. Sound Vibr. 332 (10) (2013) 2611-2626.
17. J. Wang, Q. He, F. Kong, Multi-scale manifold for machinery fault diagnosis, in: 8th World Congress on Engineering Asset Management (8th WCEAM), Hong Kong, 2013.
18. D.F. Dwyer, Detection of non-Gaussian signals by frequency domain kurtosis estimation, in: Proceedings of the International Conference on Acoustic, Speech, and Signal Processing, Boston 1984, pp. 607-610.
19. J. Antoni, The spectral kurtosis: a useful tool for characterizing non-stationary signals, Mech. Syst. Signal Process. 20 (2006) 282-307.
20. J. Antoni, R.B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mech. Syst. Signal Process. 20 (2006) 308-331.
21. J. Antoni, Fast computation of the kurtogram for the detection of transient faults, Mech. Syst. Signal Process. 21 (2007) 108-124.
22. L. Shi, R.B. Randall, J. Antoni, Rolling element bearing fault detection using improved envelope analysis, in: Proceedings of the Eighth International Conference on Vibrations in Rotating Machinery, Swansea, Wales, 2004, pp. 301-311.
23. N. Sawalhi, R.B. Randall, Spectral kurtosis optimization for rolling element bearings, in: Proceedings of the Eighth International Symposium on Signal Processing and its Applications, Sydney, 2005, pp. 839-842.
24. Y. Lei, J. Lin, Z. He, Y. Zi, Application of an improved kurtogram method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process. 25 (2011) 1738-174 9.
25. T. Barszcz, A. JabŁoński, A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram, Mech. Syst. Signal Process. 25 (2011) 431-451.
26. D. Wang, P.W. Tse, K.L. Tsui, An enhanced Kurtogram method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process. 35 (2013) 176-199.
27. P.W. Tse, 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 - Parts 1 and 2, Mech. Syst. Signal Process. 40 (2) (2013) 499-519.
28. J. Wang, Q. He, Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery, J. Sound Vibr. 333 (11) (2014) 2450-2464.
29. S.M. Kay, The effects of noise on the autoregressive spectral estimator, IEEE Trans. Acoust. Speech Signal Process. 27 (5) (1979) 478-485.
30. J.B. Tenenbaum, V. Silva, J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) 2319-2323.
31. S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290 (2000) 2323-2326.
32. Z. Zhang, H. Zha, Principal manifolds and nonlinear dimensionality reduction via tangent space alignment, SIAM J. Sci. Comput. 26 (1) (2005) 313-338.
33. D. Yu, J. Cheng, Y. Yang, Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings, Mech. Syst. Signal Process. 19 (2) (2005) 259-270.
34. CWRU Bearing Data Center, seeded fault test data. Available: (http://csegroups.case.edu/bearingdatacenter/home) (accessible in 2013).