Skip to main content
Log in

An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

An improved ensemble empirical mode decomposition (EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. LIU H W, DONG R H, FENG F. Study on injecting forming technology of railway locomotive bearing retainer [J]. Advanced Materials Research, 2011, 2: 1223–1229.

    Google Scholar 

  2. YU Z W, XU X L, GUO X Y. Failure investigation of a locomotive turbocharger main shaft and bearing sleeve [J]. Journal of Failure Analysis and Prevention, 2011, 11(2): 167–174.

    Article  Google Scholar 

  3. ZHI P, QIU H B, ZHANG H L. Design on on-line monitoring system for locomotive bearing fault based on embedded technology [J]. Railway Computer Application. 2009, 18(10): 35–43. (in Chinese)

    Google Scholar 

  4. LEI Y G, HE Z J, ZI Y Y. EEMD method and WNN for fault diagnosis of locomotive roller bearings [J]. Expert Systems With Applications, 2011, 38: 7334–7341.

    Article  Google Scholar 

  5. LEI Y G, HAN D, LIN J, HE Z. Planetary gearbox fault diagnosis using an adaptive stochastic resonance method [J]. Mechanical Systems and Signal Processing, 2013, 38: 113–124.

    Article  Google Scholar 

  6. LEI Y, LIN J, HE Z, ZI Y. Application of an improved kurtogram method for fault diagnosis of rolling element bearings [J]. Mechanical Systems and Signal Processing, 2011, 25: 1738–1749.

    Article  Google Scholar 

  7. ZHANG J, YAN R, GAO R X, FENG Z. Performance enhancement of ensemble empirical mode decomposition [J]. Mechanical Systems and Signal Processing, 2010, 24: 2104–2123.

    Article  Google Scholar 

  8. TSAKALOZOS N, DRAKAKIS K, RICKARD S. A formal study of the nonlinearity and consistency of the empirical mode decomposition [J]. Signal Process, 2011, 92: 1961–1969.

    Article  Google Scholar 

  9. YAN R, GAO R X. Rotary machine health diagnosis based on empirical mode decomposition [J]. Journal of Vibration and Acoustics-Transaction of the Asme, 2008, 130(2): 1–12.

    Article  Google Scholar 

  10. SHEN C Q, HE Q B, KONG F R, TSE P W. A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2013, 227: 1362–1370.

    Google Scholar 

  11. WANG S, CHEN X, CAI G, CHEN B, LI X, HE Z. Matching demodulation transform and synchrosqueezing in time-frequency analysis [J]. IEEE Transactions on Signal Processing, 2014, 1(62): 69–84.

    Article  MathSciNet  Google Scholar 

  12. BAKKER Q J, GIBSON C, WILSON P, LOHSE N, POPOV A A. Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition [J]. Mechanical System and Signal Processing, 2015, 62: 395–414.

    Article  Google Scholar 

  13. LEI Y, LIN J, HE Z, ZUO M J. A review on empirical mode decomposition in fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2013, 35: 108–126.

    Article  Google Scholar 

  14. HU X Y, PENG S L, HWANG W L. EMD Revisited: A new understanding of the envelope and resolving the mode-mixing problem in AM-FM signals [J]. IEEE Transactions on Signal Processing, 2012, 60(3): 1075–1086.

    Article  MathSciNet  Google Scholar 

  15. YI J B, QI HUANG Q. The study of low frequency oscillations parameters identification in power system based on improved HHT method [J]. Advanced Materials Research, 2012 (433): 781–788.

    Article  Google Scholar 

  16. HE Z, SHEN Y, WANG Q, WANG Y, FENG N Z, MA L Y. Mitigating end effects of EMD using non-equidistance grey model [J]. Journal of Systems Engineering and Electronics, 2012, 23(4): 603–614.

    Article  Google Scholar 

  17. WU Z H, HUANG N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1: 1–41.

    Article  Google Scholar 

  18. LEI Y, HE Z, ZI Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2009, 23: 1327–1338.

    Article  Google Scholar 

  19. LEI Y G, Li N P, LIN J, WANG S Z. Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition [J]. Sensors, 2013, 13(12): 16950–16964.

    Article  Google Scholar 

  20. RATO R T, ORTIGUEIRA M D, BATISTA A G. On the HHT, its problems, and some solutions [J]. Mechanical Systems and Signal Processing, 2008, 22: 1374–1394.

    Article  Google Scholar 

  21. SHEN C Q, LIU F, WANG D. A doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis [J]. Sensors, 2013, 13(11): 15726–15746.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chun-sheng Wang  (王春生).

Additional information

Foundation item: Project(61573381) supported by the National Natural Science Foundation of China; Project(2012AA051601) supported by the National High-tech Research and Development Program of China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Cs., Sha, Cy., Su, M. et al. An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter. J. Cent. South Univ. 24, 478–488 (2017). https://doi.org/10.1007/s11771-017-3450-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-017-3450-8

Key words

Navigation