access icon free Application of the multi-scale enveloping spectrogram to detect weak faults in a wind turbine gearbox

The gearbox of a wind turbine involves multiple rotating components, each having a potential to be affected by a fault. Detecting weak faults of these components with traditional demodulation analysis is challenging. Multi-scale enveloping spectrogram (MuSEnS) decomposes a vibration signal into different frequency bands while simultaneously generating the corresponding envelope spectra. In this study, a MuSEnS-based diagnosis approach is applied to detect faults affecting the intermediate stage of a gearbox installed in an operating wind turbine. The MuSEnSs of 12 vibration channels have allowed to identify multiple fault features, including the weak fault of the big gear on the sun shaft. The effectiveness of the proposed fault diagnosis approach has been tested with industrial data and the faults themself have been confirmed with the disassembled gears.

Inspec keywords: vibrations; wind turbines; gears; shafts; fault diagnosis

Other keywords: wind turbine gearbox; envelope spectra; demodulation analysis; weak fault detection; sun shaft; frequency bands; multiscale enveloping spectrogram; multiple-rotating components; vibration channel; fault diagnosis approach; MuSEnS-based diagnosis approach; MuSEnS; vibration signal; fault feature identification

Subjects: Wind power plants

References

    1. 1)
      • 14. Li, J.M., Chen, X.F., Du, Z.H., et al: ‘A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis’, Renew. Energy, 2013, 60, (4), pp. 719.
    2. 2)
      • 11. Lapira, E., Brisset, D., Ardakani, H.D., et al: ‘Wind turbine performance assessment using multi-regime modeling approach’, Renew. Energy, 2012, 45, pp. 8695.
    3. 3)
      • 12. Herp, J., Pedersen, N.L., Nadimi, E.S.: ‘Wind turbine performance analysis based on multivariate higher order moments and Bayesian classifiers’, Control Eng. Pract., 2016, 49, pp. 204211.
    4. 4)
      • 10. Feng, Z.P., Zuo, M.J.: ‘Fault diagnosis of planetary gearboxes via torsional vibration signal analysis’, Mech. Syst. Signal Process., 2013, 36, (2), pp. 401421.
    5. 5)
      • 6. Wymore, M.L., Dam, J.E.V., Ceylan, H., et al: ‘A survey of health monitoring systems for wind turbines’, Renew. Sustain. Energy Rev., 2015, 52, pp. 976990.
    6. 6)
      • 15. Teng, W., Wang, F., Zhang, K.L., et al: ‘Pitting fault detection of a wind turbine gearbox using empirical mode decomposition’, Strojniški vestnik – J. Mech. Eng., 2014, 60, (1), pp. 1220.
    7. 7)
      • 18. Feng, Z.P., Zuo, M.J.: ‘Vibration signal models for fault diagnosis of planetary gearboxes’, J. Sound Vib., 2012, 331, (22), pp. 49194939.
    8. 8)
      • 7. Tchakoua, P., Wamkeue, R., Ouhrouche, M., et al: ‘Wind turbine condition monitoring: state-of-the-art review, new trends, and future challengesj’, Energies, 2014, 7, pp. 25952630.
    9. 9)
      • 22. Yan, R.Q., Gao, R.X.: ‘Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis’, Tribol. Int., 2009, 42, (2), pp. 293302.
    10. 10)
      • 23. Wang, J.J., Gao, R.X., Yan, R.Q.: ‘Multi-scale enveloping order spectrogram for rotating machine health diagnosis’, Mech. Syst. Signal Process., 2014, 46, (1), pp. 2844.
    11. 11)
      • 9. Zhang, Z., Verma, A., Kusiak, A.: ‘Fault analysis and condition monitoring of the wind turbine gearbox’, IEEE Trans. Energy Convers., 2012, 27, (2), pp. 526535.
    12. 12)
      • 13. Lei, Y.G., Han, D., Lin, J., et al: ‘Planetary gearbox fault diagnosis using an adaptive stochastic resonance method’, Mech. Syst. Signal Process., 2013, 38, (1), pp. 113124.
    13. 13)
      • 21. Feng, Z.P., Liang, M.: ‘Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis’, Renew. Energy, 2014, 66, pp. 468477.
    14. 14)
      • 1. Becker, E., Poste, P.: ‘Keeping the blades turning: condition monitoring of wind turbine gears’, Refocus, 2006, 7, (2), pp. 2632.
    15. 15)
      • 5. Amirata, Y., Benbouzida, M.E.H., Al-Ahmara, E., et al: ‘A brief status on condition monitoring and fault diagnosis in wind energy conversion systems’, Renew. Sustain. Energy Rev., 2009, 13, (9), pp. 26292636.
    16. 16)
      • 3. Wiggelinkhuizen, E., Verbruggen, T., Braam, H., et al: ‘Assessment of condition monitoring techniques for offshore wind farms’, J. Solar Energy Eng., 2008, 130, (3), pp. 0310041:9.
    17. 17)
      • 4. Salem, A.A., Abu-Siada, A., Islam, S.: ‘Condition monitoring techniques of the wind turbines gearbox and rotor’, Int. J. Electr. Energy, 2014, 2, (1), pp. 5356.
    18. 18)
      • 8. Yang, W., Tavner, P.J., Wilkinson, M.R.: ‘Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train’, IET Renew. Power Gener., 2009, 3, (1), pp. 111.
    19. 19)
      • 20. Feng, Z.P., Chen, X., Liang, M.: ‘Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions’, Mech. Syst. Signal Process., 2015, 52-53, pp. 360375.
    20. 20)
      • 16. Sun, H., Zi, Y., He, Z.: ‘Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold’, Appl. Acoust., 2014, 77, pp. 122129.
    21. 21)
      • 17. Barszcz, T., Randall, R.B.: ‘Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine’, Mech. Syst. Signal Process., 2009, 23, (4), pp. 13521365.
    22. 22)
      • 2. Ribrant, J., Bertling, L.M.: ‘Survey of failures in wind power systems with focus on Swedish wind power plants during 1997-2005’, IEEE Trans. Energy Convers., 2007, 22, (1), pp. 167173.
    23. 23)
      • 19. Feng, Z.P., 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’, Renew. Energy, 2012, 47, (1), pp. 112126.
    24. 24)
      • 24. Feldman, M.: ‘Hilbert transform in vibration analysis’, Mech. Syst. Signal Process., 2011, 25, (3), pp. 735802.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0722
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0722
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading