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

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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.

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References

  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 Access

  2. Liang SY, Li Y, Billington SA, Zhang C, Shiroishi J, Kurfess TR, Danyluk S (2014) Adaptive prognostics for rotary machineries. Procedia Engineering 86:852–857

    Article  Google Scholar 

  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. 137–165

  4. Randall RB, Antoni J (2011) Rolling element bearing diagnostics—a tutorial. Mech Syst Signal Process 25(2):485–520

    Article  Google Scholar 

  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. IEEE

  6. Randall RB (2011) Vibration-based condition monitoring: industrial, aerospace and automotive applications John Wiley & Sons

  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):072501

    Article  Google Scholar 

  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 29:362–376

    Article  Google Scholar 

  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 367:233–249

    Article  Google Scholar 

  10. Feng Z, Chen X, Wang T (2017) Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions. J Sound Vib 400:71–85

    Article  Google Scholar 

  11. McFadden P, Smith J (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int 17(1):3–10

    Article  Google Scholar 

  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):945–962

    Article  Google Scholar 

  13. Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20(2):282–307

    Article  Google Scholar 

  14. Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(1):108–124

    Article  Google Scholar 

  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):308–331

    Article  Google Scholar 

  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 Society

  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):108–126

    Article  Google Scholar 

  18. Daubechies I, Lu J, Wu H-T (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261

    Article  MathSciNet  Google Scholar 

  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. IEEE

  20. Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010

    Article  MathSciNet  Google Scholar 

  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 89:80–92

    Article  Google Scholar 

  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):23–35

    Article  Google Scholar 

  23. Antoni J (2007) Cyclic spectral analysis of rolling-element bearing signals: facts and fictions. J Sound Vib 304(3–5):497–529

    Article  Google Scholar 

  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 89:16–27

    Article  Google Scholar 

  25. Dybała J, Zimroz R (2014) Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Appl Acoust 77:195–203

    Article  Google Scholar 

  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):127–140

    Article  Google Scholar 

  27. Zhang J, Yan R, Gao RX, Feng Z (2010) Performance enhancement of ensemble empirical mode decomposition. Mech Syst Signal Process 24(7):2104–2123

    Article  Google Scholar 

  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):1327–1338

    Article  Google Scholar 

  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 81:88–107

    Article  Google Scholar 

  30. Daubechies I (1992) Ten lectures on wavelets. Vol. 61 Siam

  31. Chen SS, Donoho DL, Saunders MA (2001) Atomic decomposition by basis pursuit. SIAM Rev 43(1):129–159

    Article  MathSciNet  Google Scholar 

  32. Tibshirani R (1996) Regression shrinkage and selection via the lasso. JR Stat Soc Series B (Methodological):267–288

    MathSciNet  MATH  Google Scholar 

  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 Geophysicists

  34. Marjanovic G, Solo V (2012) On lq optimization and matrix completion. IEEE Trans Signal Process 60(11):5714–5724

    Article  MathSciNet  Google Scholar 

  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):6976–6994

    Article  Google Scholar 

  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):1066–1090

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Yanfei Lu & Steven Y. Liang

  2. Department of Statistics at the University of Georgia, Athens, GA, 30602, USA

    Rui Xie

  3. College of Mechanical Engineering, Donghua University, Shanghai, 201620, China

    Steven Y. Liang

Authors
  1. Yanfei Lu
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  2. Rui Xie
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  3. Steven Y. Liang
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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.

Corresponding author

Correspondence to Yanfei Lu.

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Lu, Y., Xie, R. & Liang, S.Y. Detection of weak fault using sparse empirical wavelet transform for cyclic fault. Int J Adv Manuf Technol 99, 1195–1201 (2018). https://doi.org/10.1007/s00170-018-2553-1

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  • DOI: https://doi.org/10.1007/s00170-018-2553-1

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