Frontiers of Mechanical Engineering

, Volume 12, Issue 3, pp 427–439 | Cite as

Tacholess order-tracking approach for wind turbine gearbox fault detection

  • Yi Wang
  • Yong Xie
  • Guanghua Xu
  • Sicong Zhang
  • Chenggang Hou
Research Article
  • 72 Downloads

Abstract

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Keywords

wind turbine variable-speed operating conditions Vold-Kalman filtering tacholess order tracking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This research was supported by the Foundation of National 863 Plan of China (Grant No. 2015AA043004). Special thanks should be expressed to the editors and anonymous reviewers for their valuable suggestions.

References

  1. 1.
    McFadden P D, Smith J D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique—A review. Tribology International, 1984, 17(1): 3–10CrossRefGoogle Scholar
  2. 2.
    Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, 2001, 15(2): 287–302CrossRefGoogle Scholar
  3. 3.
    Makowski R A, Zimroz R. Adaptive Bearings Vibration Modelling for Diagnosis. Berlin: Springer, 2011CrossRefGoogle Scholar
  4. 4.
    Wang Y, Xu G, Liang L, et al. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing, 2015, 54–55: 259–276CrossRefGoogle Scholar
  5. 5.
    Wang D, Tse P W, Tsui K L. An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2013, 35(1–2): 176–199CrossRefGoogle Scholar
  6. 6.
    Sawalhi N, Randall R B, Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 2007, 21(6): 2616–2633CrossRefGoogle Scholar
  7. 7.
    Barszcz T, JabŁoński A. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, 2011, 25 (1): 431–451CrossRefGoogle Scholar
  8. 8.
    Urbanek J, Antoni J, Barszcz T. Detection of signal component modulations using modulation intensity distribution. Mechanical Systems and Signal Processing, 2012, 28: 399–413CrossRefGoogle Scholar
  9. 9.
    Samuel P D, Pines D J. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration, 2005, 282(1–2): 475–508CrossRefGoogle Scholar
  10. 10.
    Combet F, Gelman L. Optimal filtering of gear signals for early damage detection based on the spectral kurtosis. Mechanical Systems and Signal Processing, 2009, 23(3): 652–668CrossRefGoogle Scholar
  11. 11.
    Brie D, Tomczak M, Oehlmann H, et al. Gear crack detection by adaptive amplitude and phase demodulation. Mechanical Systems and Signal Processing, 1997, 11(1): 149–167CrossRefGoogle Scholar
  12. 12.
    Zimroz R, Bartkowiak A. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing, 2013, 38(1): 237–247CrossRefGoogle Scholar
  13. 13.
    Zimroz R, Bartelmus W. Gearbox condition estimation using cyclostationary properties of vibration signal. Key Engineering Materials, 2009, 413–414: 471–478CrossRefGoogle Scholar
  14. 14.
    Zimroz R, Bartkowiak A. Investigation on spectral structure of gearbox vibration signals by principal component analysis for condition monitoring purposes. Journal of Physics: Conference Series, 2011, 305(1): 012075Google Scholar
  15. 15.
    Liu W, Tang B, Han J, et al. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, 2015, 44: 466–472CrossRefGoogle Scholar
  16. 16.
    Tang B, Liu W, Song T. Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution. Renewable Energy, 2010, 35(12): 2862–2866CrossRefGoogle Scholar
  17. 17.
    Jiang Y, Tang B, Qin Y, et al. Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renewable Energy, 2011, 36(8): 2146–2153CrossRefGoogle Scholar
  18. 18.
    Tang B, Song T, Li F, et al. Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renewable Energy, 2014, 62: 1–9CrossRefGoogle Scholar
  19. 19.
    Yang W, Tavner P J, Tian W. Wind turbine condition monitoring based on an improved spline-kernelled Chirplet transform. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6565–6574CrossRefGoogle Scholar
  20. 20.
    Feng Z, Liang M, Zhang Y, et al. Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renewable Energy, 2012, 47: 112–126CrossRefGoogle Scholar
  21. 21.
    Xu Y, Chen J. Characterizing nonstationary wind speed using empirical mode decomposition. Journal of Structural Engineering, 2004, 130(6): 912–920CrossRefGoogle Scholar
  22. 22.
    An X, Jiang D, Li S, et al. Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine. Energy, 2011, 36(9): 5508–5520CrossRefGoogle Scholar
  23. 23.
    Yang W, Court R, Tavner P J, et al. Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring. Journal of Sound and Vibration, 2011, 330(15): 3766–3782CrossRefGoogle Scholar
  24. 24.
    Liu H, Chen C, Tian H, et al. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renewable Energy, 2012, 48: 545–556CrossRefGoogle Scholar
  25. 25.
    Feng Z, Liang M. Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel timefrequency analysis. Renewable Energy, 2014, 66: 468–477CrossRefGoogle Scholar
  26. 26.
    Randall R B, Antoni J. Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 2011, 25(2): 485–520CrossRefGoogle Scholar
  27. 27.
    Borghesani P, Ricci R, Chatterton S, et al. A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions. Mechanical Systems and Signal Processing, 2013, 38(1): 23–35CrossRefGoogle Scholar
  28. 28.
    McFadden P D, Toozhy M M. Application of synchronous averaging to vibration monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 2000, 14(6): 891–906CrossRefGoogle Scholar
  29. 29.
    Fyfe K R, Munck E D S. Analysis of computed order tracking. Mechanical Systems and Signal Processing, 1997, 11(2): 187–205CrossRefGoogle Scholar
  30. 30.
    Wang T, Liang M, Li J, et al. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis. Mechanical Systems and Signal Processing, 2014, 45(1): 139–153CrossRefGoogle Scholar
  31. 31.
    Zhao M, Lin J, Xu X, et al. Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds. Sensors (Basel), 2013, 13(8): 10856–10875CrossRefGoogle Scholar
  32. 32.
    Wang Y, Xu G, Zhang Q, et al. Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions. Journal of Sound and Vibration, 2015, 348: 381–396CrossRefGoogle Scholar
  33. 33.
    Wang Y, Xu G, Luo A, et al. An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection. Journal of Sound and Vibration, 2016, 367: 233–249CrossRefGoogle Scholar
  34. 34.
    Bonnardot F, El Badaoui M, Randall R B, et al. Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Mechanical Systems and Signal Processing, 2005, 19(4): 766–785CrossRefGoogle Scholar
  35. 35.
    Zhao M, Lin J, Wang X, et al. A tacho-less order tracking technique for large speed variations. Mechanical Systems and Signal Processing, 2013, 40(1): 76–90CrossRefGoogle Scholar
  36. 36.
    Boashash B. Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proceedings of the IEEE, 1992, 80(4): 520–538CrossRefGoogle Scholar
  37. 37.
    Boashash B. Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications. Proceedings of the IEEE, 1992, 80(4): 540–568CrossRefGoogle Scholar
  38. 38.
    Rodopoulos K, Yiakopoulos C, Antoniadis I. A parametric approach for the estimation of the instantaneous speed of rotating machinery. Mechanical Systems and Signal Processing, 2014, 44(1–2): 31–46CrossRefGoogle Scholar
  39. 39.
    Feng Z, Chen X, Liang M. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Mechanical Systems and Signal Processing, 2015, 52–53: 360–375CrossRefGoogle Scholar
  40. 40.
    Combet F, Gelman L. An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor. Mechanical Systems and Signal Processing, 2007, 21(6): 2590–2606CrossRefGoogle Scholar
  41. 41.
    Urbanek J, Barszcz T, Sawalhi N, et al. Comparison of amplitudebased and phase-based methods for speed tracking in application to wind turbines. Metrology and Measurement Systems, 2011, 18(2): 295–304CrossRefGoogle Scholar
  42. 42.
    Jabloun M, Martin N, Leonard F, et al. Estimation of the instantaneous amplitude and frequency of non-stationary shorttime signals. Signal Processing, 2008, 88(7): 1636–1655CrossRefMATHGoogle Scholar
  43. 43.
    Chandra Sekhar S, Sreenivas T V. Effect of interpolation on PWVD computation and instantaneous frequency estimation. Signal Processing, 2004, 84(1): 107–116CrossRefMATHGoogle Scholar
  44. 44.
    Shui P, Bao Z, Su H. Nonparametric detection of FM signals using time-frequency ridge energy. IEEE Transactions on Signal Processing, 2008, 56(5): 1749–1760MathSciNetCrossRefGoogle Scholar
  45. 45.
    Combet F, Zimroz R. A new method for the estimation of the instantaneous speed relative fluctuation in a vibration signal based on the short time scale transform. Mechanical Systems and Signal Processing, 2009, 23(4): 1382–1397CrossRefGoogle Scholar
  46. 46.
    Peng Z, Meng G, Chu F, et al. Polynomial chirplet transform with application to instantaneous frequency estimation. IEEE Transactions on Instrumentation and Measurement, 2011, 60(9): 3222–3229CrossRefGoogle Scholar
  47. 47.
    Gryllias K C, Antoniadis I A. Estimation of the instantaneous rotation speed using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing, 2013, 38(1): 78–95CrossRefGoogle Scholar
  48. 48.
    Urbanek J, Barszcz T, Antoni J. A two-step procedure for estimation of instantaneous rotational speed with large fluctuations. Mechanical Systems and Signal Processing, 2013, 38(1): 96–102CrossRefGoogle Scholar
  49. 49.
    Rankine L, Mesbah M, Boashash B. IF estimation for multicomponent signals using image processing techniques in the timefrequency domain. Signal Processing, 2007, 87(6): 1234–1250CrossRefMATHGoogle Scholar
  50. 50.
    Yang Y, Peng Z, Meng G, et al. Characterize highly oscillating frequency modulation using generalized Warblet transform. Mechanical Systems and Signal Processing, 2012, 26: 128–140CrossRefGoogle Scholar
  51. 51.
    Yang Y, Dong X, Peng Z, et al. Vibration signal analysis using parameterized time-frequency method for features extraction of varying-speed rotary machinery. Journal of Sound and Vibration, 2015, 335: 350–366CrossRefGoogle Scholar
  52. 52.
    Liu H, Cartwright A N, Basaran C. Moiré interferogram phase extraction: A ridge detection algorithm for continuous wavelet transforms. Applied Optics, 2004, 43(4): 850–857CrossRefGoogle Scholar
  53. 53.
    Feng Z, Chu F, Zuo M. Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation. Journal of Sound and Vibration, 2011, 330(6): 1225–1243CrossRefGoogle Scholar
  54. 54.
    Hyers R W, McGowan J G, Sullivan K L, et al. Condition monitoring and prognosis of utility scale wind turbines. Energy Materials, 2006, 1(3): 187–203CrossRefGoogle Scholar
  55. 55.
    Peng Z, Chu F. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 2004, 18(2): 199–221CrossRefGoogle Scholar
  56. 56.
    Vold H, Leuridan J. High resolution order tracking at extreme slew rates, using Kalman tracking filters. Shock and Vibration, 1995, 2 (6): 507–515CrossRefGoogle Scholar
  57. 57.
    Qin S. Feng Z, Liang M. Application of Vold-Kalman and higher order energy separation to fault diagnosis of planetary gearbox under time-varying conditions. Journal of Vibration Engineering, 2015, 28(5): 841–845 (in Chinese)Google Scholar
  58. 58.
    Pan M C, Lin Y F. Further exploration of Vold-Kalman filtering order tracking with shaft-speed information—I: Theoretical part, numerical implementation and parameter investigations. Mechanical Systems and Signal Processing, 2006, 20(5): 1134–1154CrossRefGoogle Scholar
  59. 59.
    Sharma V, Parey A. A review of gear fault diagnosis using various condition indicators. Procedia Engineering, 2016, 144: 253–263CrossRefGoogle Scholar
  60. 60.
    Cheng J, Yang Y, Yu D. The envelope order spectrum based on generalized demodulation time-frequency analysis and its application to gear fault diagnosis. Mechanical Systems and Signal Processing, 2010, 24(2): 508–521CrossRefGoogle Scholar
  61. 61.
    Ma J, Li C. Gear defect detection through model-based wideband demodulation of vibrations. Mechanical Systems and Signal Processing, 1996, 10(5): 653–665CrossRefGoogle Scholar
  62. 62.
    Randall R B, Antoni J, Chobsaard S. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing, 2001, 15(5): 945–962CrossRefGoogle Scholar
  63. 63.
    Feng Z, Chen X, Liang M. Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox. Mechanical Systems and Signal Processing, 76–77: 242–264Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yi Wang
    • 1
    • 2
  • Yong Xie
    • 2
  • Guanghua Xu
    • 2
    • 3
  • Sicong Zhang
    • 2
  • Chenggang Hou
    • 2
  1. 1.School of Mechanical EngineeringChongqing UniversityChongqingChina
  2. 2.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anChina

Personalised recommendations