Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Comparison of wind turbine gearbox vibration analysis algorithms based on feature extraction and classification

Health state assessment of wind turbine components has become a vital aspect of wind farm operations in order to reduce maintenance costs. The gearbox is one of the most costly components to replace and it is usually monitored through vibration condition monitoring. This study aims to present a review of the most popular existing gear vibration diagnostic methods. Features are extracted from the vibration signals based on each method and are used as input in pattern recognition algorithms. Classification of each signal is achieved based on its health state. This is demonstrated in a case study using historic vibration data acquired from operational wind turbines. The data collection starts from a healthy operating condition and leads towards a gear failure. The results of various diagnostic algorithms are compared based on their classification accuracy.

References

    1. 1)
      • 28. Hanna, J., Hatch, C., Kalb, M., et al: ‘Detection of wind turbine gear tooth defects using sideband energy ratio™’. China Wind Power 2011, Beijing, China, 19–21 October 2011.
    2. 2)
      • 52. Koukoura, S., Carroll, J., McDonald, A.: ‘Wind turbine intelligent gear fault identification’. Annual Conf. of the PHM Society, St Petersburg, FL, USA, 2017.
    3. 3)
      • 14. Feng, Z., Zuo, M.J.: ‘Vibration signal models for fault diagnosis of planetary gearboxes’, J. Sound Vib., 2012, 331, (22), pp. 49194939.
    4. 4)
      • 16. Jardine, A.K., Lin, D., Banjevic, D.: ‘A review on machinery diagnostics and prognostics implementing condition-based maintenance’, Mech. Syst. Signal Process. ., 2006, 20, (7), pp. 14831510.
    5. 5)
      • 15. Hong, L., Dhupia, J.S., Sheng, S.: ‘An explanation of frequency features enabling detection of faults in equally spaced planetary gearbox’, Mech. Mach. Theory, 2014, 73, pp. 169183.
    6. 6)
      • 34. Li, Y., Xu, M., Wei, Y., et al: ‘A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree’, Measurement, 2016, 77, pp. 8094.
    7. 7)
      • 17. Abouel Seoud, S.A.: ‘Fault detection enhancement in wind turbine planetary gearbox via stationary vibration waveform data’, J. Low Freq. Noise Vib. Act. Control, 2018, 37, (3), pp. 477494.
    8. 8)
      • 54. Fyfe, K., Munck, E.: ‘Analysis of computed order tracking’, Mech. Syst. Signal Process., 1997, 11, (2), pp. 187205.
    9. 9)
      • 8. Amirat, Y., Benbouzid, M.E.H., Al Ahmar, E., et al: ‘A brief status on condition monitoring and fault diagnosis in wind energy conversion systems’, Renew. Sust. Energy Rev., 2009, 13, (9), pp. 26292636.
    10. 10)
      • 43. Christopher, M.B.: ‘Pattern recognition and machine learning’ (Springer-Verlag, New York, 2016).
    11. 11)
      • 45. Catmull, S.: ‘Self-organising map based condition monitoring of wind turbines’. EWEA Annual Conf., Brussels, Belgium, 2011, vol. 2011.
    12. 12)
      • 38. Wang, W., McFadden, P.: ‘Application of wavelets to gearbox vibration signals for fault detection’, J. Sound Vib., 1996, 192, (5), pp. 927939.
    13. 13)
      • 10. Crabtree, C.J., Zappalá, D., Tavner, P.J.: ‘Survey of commercially available condition monitoring systems for wind turbines’, 2014.
    14. 14)
      • 35. Teng, W., Wang, F., Zhang, K., et al: ‘Pitting fault detection of a wind turbine gearbox using empirical mode decomposition’, Strojniški Vestnik-J. Mech. Eng., 2014, 60, (1), pp. 1220.
    15. 15)
      • 57. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis’. Proc. of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, London, UK, 1998, vol. 454, pp. 903995.
    16. 16)
      • 40. Tang, B., Liu, W., Song, T.: ‘Wind turbine fault diagnosis based on morlet wavelet transformation and wigner-ville distribution’, Renew. Energy, 2010, 35, (12), pp. 28622866.
    17. 17)
      • 42. Teng, W., Ding, X., Zhang, X., et al: ‘Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform’, Renew. Energy, 2016, 93, pp. 591598.
    18. 18)
      • 41. Feng, Z., 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.
    19. 19)
      • 50. Leahy, K., Hu, R.L., Konstantakopoulos, I.C., et al: ‘Diagnosing wind turbine faults using machine learning techniques applied to operational data’. 2016 IEEE Int. Conf. on Prognostics and Health Management (ICPHM), Ottawa, Canada, 2016, pp. 18.
    20. 20)
      • 33. Capdessus, C., Sidahmed, M., Lacoume, J.: ‘Cyclostationary processes: application in gear faults early diagnosis’, Mech. Syst. Signal Process., 2000, 14, (3), pp. 371385.
    21. 21)
      • 60. Wang, L., Zhang, Z., Long, H., et al: ‘Wind turbine gearbox failure identification with deep neural networks’, IEEE Trans. Ind. Inf., 2016, 13, (3), pp. 13601368.
    22. 22)
      • 5. Tian, Z., Jin, T., Wu, B., et al: ‘Condition based maintenance optimization for wind power generation systems under continuous monitoring’, Renew. Energy, 2011, 36, (5), pp. 15021509.
    23. 23)
      • 11. Feng, Y., Qiu, Y., Crabtree, C.J., et al: ‘Monitoring wind turbine gearboxes’, Wind Energy, 2013, 16, (5), pp. 728740.
    24. 24)
      • 2. Wilkinson, M., Hendriks, B., Spinato, F., et al: ‘Methodology and results of the reliawind reliability field study’. European Wind Energy Conf. and Exhibition 2010 (EWEC 2010), Sheffield, 2010, vol. 3, pp. 19842004.
    25. 25)
      • 21. Wang, W., McFadden, P.: ‘Early detection of gear failure by vibration analysis i. Calculation of the time–frequency distribution’, Mech. Syst. Signal Process., 1993, 7, (3), pp. 193203.
    26. 26)
      • 53. Shin, S.H., Kim, S., Seo, Y.H.: ‘Development of a fault monitoring technique for wind turbines using a hidden markov model’, Sensors, 2018, 18, (6), p. 1790.
    27. 27)
      • 18. Hochmann, D., Sadok, M.: ‘Theory of synchronous averaging/sup/spl omega’. 2004 IEEE Aerospace Conf. Proc., Big Sky, MT, USA, 2004, vol. 6, pp. 36363653.
    28. 28)
      • 24. Sheng, S.: ‘Wind turbine gearbox condition monitoring round robin study–vibration analysis’, Contract, 2012, 303, pp. 2753000.
    29. 29)
      • 36. 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’, Renew. Energy, 2012, 47, pp. 112126.
    30. 30)
      • 3. Crabtree, C.J.: ‘Operational and reliability analysis of offshore wind farms’. Proc. of the scientific track of the European Wind Energy Association Conf., Copenhagen, Denmark, 2012.
    31. 31)
      • 1. Ribrant, J., Bertling, L.: ‘Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005’. 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 2007, pp. 18.
    32. 32)
      • 27. 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.
    33. 33)
      • 37. Kang, S., Ma, D., Wang, Y., et al: ‘Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding’, Mech. Syst. Signal Process., 2017, 86, pp. 4057.
    34. 34)
      • 6. May, A.: ‘Operational expenditure optimisation utilising condition monitoring for offshore wind parks’. Ph. D. Thesis, University of Strathclyde, Glasgow, UK, 2016.
    35. 35)
      • 46. Gibert, K., Marti Puig, P., Cusidó, J., et al: ‘Identifying health status of wind turbines by using self organizing maps and interpretation-oriented post-processing tools’, Energies, 2018, 11, (4), p. 723.
    36. 36)
      • 4. Carroll, J., McDonald, A., McMillan, D.: ‘Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines’, Wind Energy, 2015, 19, 11071119..
    37. 37)
      • 20. Randall, R.B.: ‘A history of cepstrum analysis and its application to mechanical problems’, Mech. Syst. Signal Process., 2016, 97, 39..
    38. 38)
      • 62. Goodfellow, I., Bengio, Y., Courville, A.: ‘Deep learning’ (MIT Press, New York, NY, USA, 2016).
    39. 39)
      • 64. Huang, N.E., Wu, Z.: ‘A review on hilbert-huang transform: method and its applications to geophysical studies’, Rev. Geophys., 2008, 46, (2), pp. 228251.
    40. 40)
      • 32. Antoni, J., Xin, G., Hamzaoui, N.: ‘Fast computation of the spectral correlation’, Mech. Syst. Signal Process., 2017, 92, pp. 248277.
    41. 41)
      • 30. Antoni, J., Randall, R.: ‘The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines’, Mech. Syst. Signal Process., 2006, 20, (2), pp. 308331.
    42. 42)
      • 58. Wu, Z., Huang, N.E.: ‘Ensemble empirical mode decomposition: a noise-assisted data analysis method’, Adv. Adapt. Data. Anal., 2009, 1, (1), pp. 141.
    43. 43)
      • 22. Young, R.K.: ‘Wavelet theory and its applicationsvol. 189 (Springer Science & Business Media, Berlin, Germany, 2012).
    44. 44)
      • 56. Bogert, B.P.: ‘The quefrency alanysis of time series for echoes: cepstrum pseudo-autocovariance, cross-cepstrum, and saphe cracking’, Time Ser. Anal., 1963, 1, pp. 209243.
    45. 45)
      • 65. Hua, J., Xiong, Z., Lowey, J., et al: ‘Optimal number of features as a function of sample size for various classification rules’, Bioinformatics, 2004, 21, (8), pp. 15091515.
    46. 46)
      • 23. Staszewski, W., Tomlinson, G.: ‘Application of the wavelet transform to fault detection in a spur gear’, Mech. Syst. Signal Process., 1994, 8, (3), pp. 289307.
    47. 47)
      • 55. Vold, H., Mains, M., Blough, J.: ‘Theoretical foundations for high performance order tracking with the vold-kalman tracking filter’. SAE Technical Paper, 1997..
    48. 48)
      • 25. Zhu, J., Nostrand, T., Spiegel, C., et al: ‘Survey of condition indicators for condition monitoring systems’. Annu. Conf. Progn. Heal. Manag. Soc., Fort Worth, TX, USA, 2014, vol. 5, pp. 113.
    49. 49)
      • 63. Hsu, C.W., Chang, C.C., Lin, C.J., et al: ‘A practical guide to support vector classification’, 2003.
    50. 50)
      • 26. Bechhoefer, E., Kingsley, M.: ‘A review of time synchronous average algorithms’. Annual Conf. of the Prognostics and Health Management Society, San Diego, CA, September 2009, pp. 2433.
    51. 51)
      • 49. Santos, P., Villa, L.F., Reñones, A., et al: ‘An SVM-based solution for fault detection in wind turbines’, Sensors, 2015, 15, (3), pp. 56275648.
    52. 52)
      • 44. Korbicz, J., Koscielny, J.M., Kowalczuk, Z., et al: ‘Fault diagnosis: models, artificial intelligence, applications’ (Springer Science & Business Media, Berlin, Germany, 2012).
    53. 53)
      • 29. Randall, R.: ‘Cepstrum analysis and gearbox fault-diagnosis’, Maint. Manage. Int., 1982, 3, (3), pp. 183208.
    54. 54)
      • 12. Qiu, Y., Feng, Y., Sun, J., et al: ‘Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data’, IET Renew. Power Gener., 2016, 10, (5), pp. 661668.
    55. 55)
      • 19. McFadden, P., Smith, J.: ‘Vibration monitoring of rolling element bearings by the high-frequency resonance technique‒a review’, Tribol. Int., 1984, 17, (1), pp. 310.
    56. 56)
      • 7. Hameed, Z., Hong, Y., Cho, Y., et al: ‘Condition monitoring and fault detection of wind turbines and related algorithms: A review’, Renew. Sust. Energy Rev., 2009, 13, (1), pp. 139.
    57. 57)
      • 9. Márquez, F.P.G., Tobias, A.M., Pérez, J.M.P., et al: ‘Condition monitoring of wind turbines: techniques and methods’, Renew. Energy, 2012, 46, pp. 169178.
    58. 58)
      • 13. McFadden, P.: ‘Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration’, J. Vibr. Acoust. Stress Reliab. Design, 1986, 108, (2), pp. 165170.
    59. 59)
      • 51. Galloway, G.S., Catterson, V.M., Fay, T., et al: ‘Diagnosis of tidal turbine vibration data through deep neural networks’, 2016.
    60. 60)
      • 47. Zhao, L., Pan, Z., Shao, C., et al: ‘Application of som neural network in fault diagnosis of wind turbine’, 2015.
    61. 61)
      • 48. Yang, S., Li, W., Wang, C.: ‘The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network’. Int. Conf. on Condition Monitoring and Diagnosis, 2008 (CMD 2008), Beijing, China, 2008, pp. 13271330.
    62. 62)
      • 59. Antoni, J.: ‘Fast computation of the kurtogram for the detection of transient faults’, Mech. Syst. Signal Process. ., 2007, 21, (1), pp. 108124.
    63. 63)
      • 61. Friedman, J., Hastie, T., Tibshirani, R.: ‘The elements of statistical learning’, vol. 1 (Springer series in statistics, New York, NY, USA, 2001).
    64. 64)
      • 39. Yang, W., Tavner, P., Wilkinson, M.: ‘Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train’, IET Renew. Power Gener., 2009, 3, (1), pp. 111.
    65. 65)
      • 31. Gardner, W.: ‘Measurement of spectral correlation’, IEEE Trans. Acoust. Speech Signal Process., 1986, 34, (5), pp. 11111123.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5313
Loading

Related content

content/journals/10.1049/iet-rpg.2018.5313
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address