0
Research Papers

Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis

[+] Author and Article Information
Zhipeng Feng

School of Mechanical Engineering,
University of Science and Technology Beijing,
Beijing 100083, China
e-mail: zhipeng.feng@yahoo.com.cn

Ming J. Zuo

Department of Mechanical Engineering,
University of Alberta,
Edmonton, AB T6G 2G8, Canada
e-mail: ming.zuo@ualberta.ca

Rujiang Hao

Department of Mechanical Engineering,
Shijiazhuang Railway Institute,
Shijiazhuang 050043, China
e-mail: haorj@sjzri.edu.cn

Fulei Chu

Department of Precision Instruments
and Mechanology,
Tsinghua University,
Beijing 100084, China
e-mail: chufl@mail.tsinghua.edu.cn

Jay Lee

Department of Mechanical Engineering,
University of Cincinnati,
Cincinnati, OH 45221-0072
e-mail: jay.lee@uc.edu

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the Journal of Vibration and Acoustics. Manuscript received October 25, 2011; final manuscript received January 16, 2013; published online April 24, 2013. Assoc. Editor: Alan Palazzolo.

J. Vib. Acoust 135(3), 031013 (Apr 24, 2013) (21 pages) Paper No: VIB-11-1258; doi: 10.1115/1.4023814 History: Received October 25, 2011; Revised January 16, 2013

Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the local damage of rolling element bearings. A new method based on ensemble empirical mode decomposition (EEMD) and the Teager energy operator is proposed to extract the characteristic frequency of bearing fault. The signal is firstly decomposed into monocomponents by means of EEMD to satisfy the monocomponent requirement by the Teager energy operator. Then, the intrinsic mode function (IMF) of interest is selected according to its correlation with the original signal and its kurtosis. Next, the Teager energy operator is applied to the selected IMF to detect fault-induced impulses. Finally, Fourier transform is applied to the obtained Teager energy series to identify the repeating frequency of fault-induced periodic impulses and thereby to diagnose bearing faults. The principle of the method is illustrated by the analyses of simulated bearing vibration signals. Its effectiveness in extracting the characteristic frequency of bearing faults, and especially its performance in identifying the symptoms of weak and compound faults, are validated by the experimental signal analyses of both seeded fault experiments and a run-to-failure test. Comparison studies show its better performance than, or complements to, the traditional spectral analysis and the squared envelope spectral analysis methods.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

McFadden, P. D., and Smith, J. D., 1984, “Model for The Vibration Produced by a Single Point Defect in a Rolling Element Bearing,” J. Sound Vib., 96(1), pp. 69–82. [CrossRef]
McFadden, P. D., and Smith, J. D., 1985, “The Vibration Produced by Multiple Point Defect in a Rolling Element Bearing,” J. Sound Vib., 98(2), pp. 263–273. [CrossRef]
McFadden, P. D., and Smith, J. D., 1984, “Vibration Monitoring of Rolling Element Bearings by the High Frequency Resonance Technique—A Review,” Tribol. Int., 17(1), pp. 3–10. [CrossRef]
Feng, Z., Liu, L., and Zhang, W., 2008, “Fault Diagnosis of Rolling Element Bearings Based on Wavelet Time–Frequency Frame Decomposition,” J. Vib. Shock, 27(2), pp. 110–114 (in Chinese).
Kaiser, J. F., 1990, “On a Simple Algorithm to Calculate the ‘Energy’ of a Signal,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-90), Albuquerque, NM, April 3–6, Vol. 1, pp. 381–384. [CrossRef]
Kaiser, J. F., 1990, “On Teager's Energy Algorithm and Its Generalization to Continuous Signals,” Proceedings of 4th IEEE Digital Signal Processing Workshop, Palz, NY, September 16–19.
Kaiser, J. F., 1993, “Some Useful Properties of Teager's Energy Operators,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-93), Minneapolis, MN, April 27–30, Vol. 3, pp. 149–152. [CrossRef]
Maragos, P., Kaiser, J. F., and Quatieri, T. F., 1993, “On Amplitude and Frequency Demodulation Using Energy Operators,” IEEE Trans. Signal Process., 41(4), pp. 1532–1550. [CrossRef]
Maragos, P., Kaiser, J. F., and Quatieri, T. F., 1993, “Energy Separation in Signal Modulations With Application to Speech Analysis,” IEEE Trans. Signal Process., 41(10), pp. 3024–3051. [CrossRef]
Bovik, A. C., Maragos, P., and Quatieri, T. F., 1993, “AM-FM Energy Detection and Separation in Noise Using Multiband Energy Operators,” IEEE Trans. Signal Process., 41(12), pp. 3245–3265. [CrossRef]
Potamianos, A., and Maragos, P., 1994, “A Comparison of the Energy Operator and Hilbert Transform Approaches for Signal and Speech Demodulation,” Signal Process., 37(1), pp. 95–120. [CrossRef]
Cheng, J., Yu, D., and Yang, Y., 2007, “The Application of Energy Operator Demodulation Approach Based on EMD in Machinery Fault Diagnosis,” Mech. Syst. Signal Process., 21(2), pp. 668–677. [CrossRef]
Bassiuny, A. M., and Li, X., 2007, “Flute Breakage Detection During End Milling Using Hilbert-Huang Transform and Smoothed Nonlinear Energy Operator,” Int. J. Mach. Tools Manuf., 47(6), pp. 1011–1020. [CrossRef]
Li, H., Zheng, H., and Tang, L., 2010, “Gear Fault Detection Based on Teager-Huang Transform,” Int. J. Rotating Mach., 2010, p. 502064. [CrossRef]
Cexus, J. C., and Boudraa, A. O., 2004, “Teager-Huang Analysis Applied to Sonar Target Recognition,” Int. J. Signal Process., 1(1), 23–27.
Liang, M., and Soltani Bozchalooi, I., 2010, “An Energy Operator Approach to Joint Application of Amplitude and Frequency-Demodulations for Bearing Fault Detection,” Mech. Syst. Signal Process., 24(5), pp. 1473–1494. [CrossRef]
Soltani Bozchalooi, I., and Liang, M., 2010, “Teager Energy Operator for Multi-Modulation Extraction and Its Application for Gearbox Fault Detection,” Smart Mater. Struct., 19, p. 075008. [CrossRef]
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H., 1998, “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis,” Proc. R. Soc. London, Ser. A, 454, pp. 903–995. [CrossRef]
Wu, Z., and Huang, N. E., 2009, “Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method,” Adv. Adapt. Data Anal., 1(1), pp. 1–41. [CrossRef]
Wu, Z., and Huang, N. E., 2004, “A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method,” Proc. R. Soc. London, Ser. A, 460, pp. 1597–1611. [CrossRef]
Flandrin, P., Rilling, G., and Goncalves, P., 2004, “Empirical Mode Decomposition as a Filter Bank,” IEEE Signal Process. Lett., 11(2), pp. 112–114. [CrossRef]
Ho, D., and Randall, R. B., 2000, “Optimisation of Bearing Diagnostic Techniques Using Simulated and Actual Bearing Fault Signals,” Mech. Syst. Signal Process., 14(5), pp. 763–788. [CrossRef]
Sawalhi, N., and Randall, R. B., 2011, “Signal Pre-Whitening for Fault Detection Enhancement and Surveillance of Rolling Element Bearings,” The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Cardiff, Wales, UK, June 19–22.
Randall, R. B., and Antoni, J., 2011, “Rolling Element Bearing Diagnostics—A Tutorial,” Mech. Syst. Signal Process., 25, pp. 485–520. [CrossRef]
Qiu, H., Lee, J., Lin, J., and Yu, G., 2006, “Wavelet Filter-Based Weak Signature Detection Method and Its Application on Rolling Element Bearing Prognostics,” J. Sound Vib., 289, pp. 1066–1090. [CrossRef]
Feng, Z., Chu, F., and Zuo, M. J., 2011, “Time-Frequency Analysis of Time-Varying Modulated Signals Based on Improved Energy Separation by Iterative Generalized Demodulation,” J. Sound Vib., 30, pp. 1225–1243. [CrossRef]
Feng, Z., Wang, T., Zuo, M. J., Chu, F., and Yan, S., 2011, “Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings,” J. Phys.: Conf. Ser., 305(1), p. 012129. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Analysis result of a simulated signal. (a) Waveform. (b) Power spectrum. (c) Instantaneous Teager energy. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 2

Analysis result of a simulated signal. (a) Waveform. (b) Power spectrum. (c1) Signal and IMF1-5. (c2) IMF6-10 and residue. (d) Correlation coefficient. (e) Kurtosis of IMFs. (f) Instantaneous Teager energy of IMF1. (g) Teager energy spectrum of IMF1. (h) Squared envelope. (i) Squared envelope spectrum.

Grahic Jump Location
Fig. 4

Seeded damage on bearing elements

Grahic Jump Location
Fig. 7

Inner race–damaged bearing signal. (a) Waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c) IMF 2. (d1) Teager energy spectrum. (d2) Zoomed-in Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 5

Normal bearing signal. (a) Waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c) IMF 2. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 8

Ball-damaged bearing signal. (a) Waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c) IMF 2. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 6

Outer race–damaged bearing signal. (a1) Waveform. (a2) Zoomed-in waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c1) IMF 2. (c2) Zoomed-in IMF2. (d) Teager energy spectrum. (e1) Squared envelope. (e2) Zoomed-in squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 10

Experimental setup of bearing run-to-failure test [25]

Grahic Jump Location
Fig. 9

Compound-damaged bearing signal. (a) Waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c) IMF 3. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 12

Normal bearing signal measured on day 2. (a) Waveform. (b) Power spectrum. (c) IMF 1. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 13

Severely damaged bearing signal measured on day 8. (a) Waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c) IMF 1. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Grahic Jump Location
Fig. 11

Outer race damage of bearing 1 [25]

Grahic Jump Location
Fig. 14

Weakly damaged bearing signal measured on day 4. (a) Waveform. (b1) Power spectrum. (b2) Zoomed-in power spectrum. (c) IMF 1. (d) Teager energy spectrum. (e) Squared envelope. (f) Squared envelope spectrum.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In