Advertisement

Detection of weak fault using sparse empirical wavelet transform for cyclic fault

  • Yanfei LuEmail author
  • Rui Xie
  • Steven Y. Liang
ORIGINAL ARTICLE

Abstract

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The lq-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.

Keywords

Ball bearing Fault diagnosis Sparse matrices Wavelet transforms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Author’s contributions

Y.L. and R.X. created the model and analyzed the data; S.Y.L. provided feedback of the concept; Y.L. and R.X. wrote the paper.

References

  1. 1.
    Lu Y, Li Q, Pan Z, and Liang SY (2018) Prognosis of bearing degradation using gradient variable forgetting factor RLS combined with time series model. IEEE AccessGoogle Scholar
  2. 2.
    Liang SY, Li Y, Billington SA, Zhang C, Shiroishi J, Kurfess TR, Danyluk S (2014) Adaptive prognostics for rotary machineries. Procedia Engineering 86:852–857CrossRefGoogle Scholar
  3. 3.
    Kurfess TR, Billington S, and Liang SY (2006) Advanced diagnostic and prognostic techniques for rolling element bearings, in Condition monitoring and control for intelligent manufacturing. Springer. p. 137–165Google Scholar
  4. 4.
    Randall RB, Antoni J (2011) Rolling element bearing diagnostics—a tutorial. Mech Syst Signal Process 25(2):485–520CrossRefGoogle Scholar
  5. 5.
    Lu Y, Li Q, and Liang SY (2017) Adaptive prognosis of bearing degradation based on wavelet decomposition assisted ARMA model. In Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2017 IEEE 2nd Information. IEEEGoogle Scholar
  6. 6.
    Randall RB (2011) Vibration-based condition monitoring: industrial, aerospace and automotive applications John Wiley & SonsGoogle Scholar
  7. 7.
    Luo H, Qiu H, Ghanime G, Hirz M, van der Merwe G (2010) Synthesized synchronous sampling technique for differential bearing damage detection. J Eng Gas Turbines Power 132(7):072501CrossRefGoogle Scholar
  8. 8.
    Siegel D, Al-Atat H, Shauche V, Liao L, Snyder J, Lee J (2012) Novel method for rolling element bearing health assessment—a tachometer-less synchronously averaged envelope feature extraction technique. Mech Syst Signal Process 29:362–376CrossRefGoogle Scholar
  9. 9.
    Wang Y, Xu G, Luo A, Liang L, Jiang K (2016) An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection. J Sound Vib 367:233–249CrossRefGoogle Scholar
  10. 10.
    Feng Z, Chen X, Wang T (2017) Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions. J Sound Vib 400:71–85CrossRefGoogle Scholar
  11. 11.
    McFadden P, Smith J (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int 17(1):3–10CrossRefGoogle Scholar
  12. 12.
    Randall RB, Antoni J, Chobsaard S (2001) 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):945–962CrossRefGoogle Scholar
  13. 13.
    Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20(2):282–307CrossRefGoogle Scholar
  14. 14.
    Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(1):108–124CrossRefGoogle Scholar
  15. 15.
    Antoni J, Randall R (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process 20(2):308–331CrossRefGoogle Scholar
  16. 16.
    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, and Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. The Royal SocietyGoogle Scholar
  17. 17.
    Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35(1–2):108–126CrossRefGoogle Scholar
  18. 18.
    Daubechies I, Lu J, Wu H-T (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261MathSciNetCrossRefGoogle Scholar
  19. 19.
    Torres ME, Colominas MA, Schlotthauer G, and Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In Acoustics, speech and signal processing (ICASSP), 2011 IEEE international conference on. IEEEGoogle Scholar
  20. 20.
    Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010MathSciNetCrossRefGoogle Scholar
  21. 21.
    Chen J, Pan J, Li Z, Zi Y, Chen X (2016) Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals. Renew Energy 89:80–92CrossRefGoogle Scholar
  22. 22.
    Borghesani P, Ricci R, Chatterton S, Pennacchi P (2013) A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions. Mech Syst Signal Process 38(1):23–35CrossRefGoogle Scholar
  23. 23.
    Antoni J (2007) Cyclic spectral analysis of rolling-element bearing signals: facts and fictions. J Sound Vib 304(3–5):497–529CrossRefGoogle Scholar
  24. 24.
    Ali JB, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27CrossRefGoogle Scholar
  25. 25.
    Dybała J, Zimroz R (2014) Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Appl Acoust 77:195–203CrossRefGoogle Scholar
  26. 26.
    Zhang X, Zhou J (2013) Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech Syst Signal Process 41(1–2):127–140CrossRefGoogle Scholar
  27. 27.
    Zhang J, Yan R, Gao RX, Feng Z (2010) Performance enhancement of ensemble empirical mode decomposition. Mech Syst Signal Process 24(7):2104–2123CrossRefGoogle Scholar
  28. 28.
    Lei Y, He Z, Zi Y (2009) Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process 23(4):1327–1338CrossRefGoogle Scholar
  29. 29.
    Kedadouche M, Thomas M, Tahan A (2016) A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis. Mech Syst Signal Process 81:88–107CrossRefGoogle Scholar
  30. 30.
    Daubechies I (1992) Ten lectures on wavelets. Vol. 61 SiamGoogle Scholar
  31. 31.
    Chen SS, Donoho DL, Saunders MA (2001) Atomic decomposition by basis pursuit. SIAM Rev 43(1):129–159MathSciNetCrossRefGoogle Scholar
  32. 32.
    Tibshirani R (1996) Regression shrinkage and selection via the lasso. JR Stat Soc Series B (Methodological):267–288MathSciNetzbMATHGoogle Scholar
  33. 33.
    Li F, Xie R, Song W, Zhao T, and Marfurt K (2017) Optimal Lq norm regularization for sparse reflectivity inversion. In 2017 SEG International Exposition and Annual Meeting. Society of Exploration GeophysicistsGoogle Scholar
  34. 34.
    Marjanovic G, Solo V (2012) On lq optimization and matrix completion. IEEE Trans Signal Process 60(11):5714–5724MathSciNetCrossRefGoogle Scholar
  35. 35.
    Raskutti G, Wainwright MJ, Yu B (2011) Minimax rates of estimation for high-dimensional linear regression over lq balls. IEEE Trans Inf Theory 57(10):6976–6994CrossRefGoogle Scholar
  36. 36.
    Qiu H, Lee J, Lin J, Yu G (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J Sound Vib 289(4–5):1066–1090CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Statistics at the University of GeorgiaAthensUSA
  3. 3.College of Mechanical Engineering, Donghua UniversityShanghaiChina

Personalised recommendations