access icon free Precise feature extraction from wind turbine condition monitoring signals by using optimised variational mode decomposition

© The Institution of Engineering and Technology
Received 09/08/2016, Accepted 03/10/2016, Revised 22/09/2016, Published 11/10/2016

Reliable condition monitoring (CM) highly relies on the correct extraction of fault-related features from CM signals. This equally applies to the CM of wind turbines (WTs). Although influenced by slowly rotating speeds and constantly varying loading, extracting fault characteristics from lengthy, nonlinear, non-stationary WT CM signals is extremely difficult, which makes WT CM one of the most challenge tasks in wind power asset management despites that lots of efforts have been spent. Attributed to the superiorities to empirical mode decomposition and its extension form Hilbert–Huang transform in dealing with nonlinear, non-stationary CM signals, the recently developed variational mode decomposition (VMD) casts a glimmer of light for the solution for this issue. However, the original proposed VMD adopts default values for both number of modes and filter frequency bandwidth. It is not adaptive to the signal being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, a precise feature extraction method based on optimised VMD is investigated. The experiments have shown that thanks to the use of the proposed optimisation strategies, the fault-related features buried in WT CM signals have been extracted out successfully.

Inspec keywords: wind turbines; condition monitoring; Hilbert transforms; wind power

Other keywords: nonlinear WT CM signals; rotating speeds; condition monitoring signals; varying loading; Hilbert-Huang transform; optimised variational mode decomposition; nonstationary WT CM signals; lengthy WT CM signals; wind power asset management; fault-related features; wind turbine; empirical mode decomposition; feature extraction; frequency bandwidth

Subjects: Wind power plants; Integral transforms in numerical analysis

References

    1. 1)
      • 9. Lei, Y.G., Lin, J., He, Z., et al: ‘A review on empirical mode decomposition in fault diagnosis of rotating machinery’, Mech. Syst. Signal Process. ., 2013, 35, (1–2), pp. 108–126.
    2. 2)
      • 30. Zhang, Y., Liu, K., Qin, L., et al: ‘Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods’, Energy Convers. Manage., 2016, 112, pp. 208–219.
    3. 3)
      • 7. Yang, W., Tavner, P., Crabtree, C., et al: ‘Cost-effective condition monitoring for wind turbines’, IEEE Trans. Ind. Electron., 2010, 57, (1), pp. 263–271.
    4. 4)
      • 18. Li, H., Zhang, Y., Zheng, H.: ‘Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings’, J. Mech. Sci. Technol., 2009, 23, (2), pp. 291–301.
    5. 5)
      • 28. Lv, Z., Tang, B., Zhou, Y., et al: ‘A novel method for mechanical fault diagnosis based on variational mode decomposition and multikernel support vector machine’, Shock Vib., 2016, 2016, 1–11.
    6. 6)
      • 8. Yang, W., Tavner, P.J., Tian, W.: ‘Wind turbine condition monitoring based on an improved spline-kernelled chirplet transform’, IEEE Trans. Ind. Electron., 2015, 62, pp. 6565–6574.
    7. 7)
      • 10. Wang, Y., Infield, D.: ‘Supervisory control and data acquisition data-based non-linear state estimation technique for wind turbine gearbox condition monitoring’, IET Renew. Power Gener., 2013, 7, (4), pp. 350–358.
    8. 8)
      • 25. Yang, W., Court, R., Tavner, P., et al: ‘Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring’, J. Sound Vib., 2011, 330, (15), pp. 3766–3782.
    9. 9)
      • 3. Tavner, P., Zappal, D., Sheng, S.: ‘Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis’, IET Renew. Power Gener.., 2014, 8, (4), pp. 380–389.
    10. 10)
      • 15. Sheen, Y.T.: ‘An envelope analysis based on the resonance modes of the mechanical system for the bearing defect diagnosis’, Measurement, 2010, 43, (7), pp. 912–934.
    11. 11)
      • 4. Yang, W., Little, C., Tavner, P., et al: ‘Data-driven technique for interpreting wind turbine condition monitoring signals’, IET Renew. Power Gener., 2014, 8, (2), pp. 151–159.
    12. 12)
      • 11. Hang, J., Zhang, J., Cheng, M.: ‘Fault diagnosis of wind turbine based on multi-sensors information fusion technology’, IET Renew. Power Gener., 2014, 8, (3), pp. 289–298.
    13. 13)
      • 24. Li, R., He, D.: ‘Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification’, IEEE Trans. Instrum. Meas., 2012, 61, (4), pp. 990–1001.
    14. 14)
      • 26. Dragomiretskiy, K., Zosso, D.: ‘Variational mode decomposition’, IEEE Trans. Signal Process., 2014, 62, (3), pp. 531–544.
    15. 15)
      • 22. Wu, Z.H., Huang, N.E.: ‘Ensemble empirical mode decomposition:a noise assisted data analysis method’, Adv. Adaptive Data Anal., 2009, 1, (1), pp. 1–41.
    16. 16)
      • 23. Rilling, G., Flandrin, P., Goncalvès, P.: ‘On empirical mode decomposition and its algorithms’. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, 2003.
    17. 17)
      • 19. Van, M., Kang, H.J., Shin, K.S.: ‘Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition’, IET Sci. Meas. Technol., 2014, 8, (6), pp. 571–578.
    18. 18)
      • 2. Zaggout, M., Tavner, P., Crabtree, C., et al: ‘Detection of rotor electrical asymmetry in wind turbine doubly-fed induction generators’, IET Renew. Power Gener., 2014, 8, (8), pp. 878–886.
    19. 19)
      • 21. Yang, Y., Cheng, J.S., Zhang, K.: ‘An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems’, Measurement, 2012, 45, pp. 561–570.
    20. 20)
      • 14. Wenxian, Y., Little, C., Court, R.: ‘S-Transform and its contribution to wind turbine condition monitoring’, Renew. Energy, 2014, 62, pp. 137–146.
    21. 21)
      • 12. Feng, Y., Qiu, Y., Crabtree, C., et al: ‘Monitoring wind turbine gearboxes’, Wind Energy, 2013, 16, (5), pp. 728–740.
    22. 22)
      • 29. Aneesh, C., Kumar, S., Hisham, P.M., et al: ‘Performance comparison of variational mode decomposition over empirical wavelet transform for the classification of power quality disturbances using support vector machine’. Proc. Int. Conf. on Information and Communication Technologies, ICICT 2014, 3–5 December 2014, Bolgatty Palace & Island Resort, Kochi, India, 2014.
    23. 23)
      • 31. Yang, W., Peng, Z., Wei, K., et al: ‘Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring’, IET Renew. Power Gener., 2016.
    24. 24)
      • 6. Yang, W., Tavner, J.P., Crabtree, J.C., et al: ‘Wind turbine condition monitoring: technical and commercial challenges’, Wind Energy, 2014, 17, (5), pp. 673–693.
    25. 25)
      • 13. Tchakoua, P., Wamkeue, R., Ouhrouche, M., et al: ‘Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges’, Energies, 2014, 7, (4), pp. 2595–2630.
    26. 26)
      • 17. Chiementin, X., Bolaers, F., Dron, J.P.: ‘Early detection of fatigue damage on rolling element bearings using adapted wavelet’, ASME J. Vib. Acoust., 2007, 129, (4), pp. 495–506.
    27. 27)
      • 16. He, W., Jiang, Z.N., Feng, K.: ‘Bearing fault detection based on optimal wavelet filter and sparse code shrinkage’, Measurement, 2009, 42, (7), pp. 1092–1102.
    28. 28)
      • 5. Barton, J.P., Watson, S.J.: ‘Analysis of electrical power data for condition monitoring of a small wind turbine’, IET Renew. Power Gener., 2013, 7, (4), pp. 341–349.
    29. 29)
      • 1. Hameed, Z., Hong, Y.S., Cho, Y.M., et al: ‘Condition monitoring and fault detection of wind turbines and related algorithms: a review’, Renew. Sustain. Energy Rev., 2009, 13, (1), pp. 1–39.
    30. 30)
      • 20. 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. R. Soc., 1998, 454, pp. 903–995.
    31. 31)
      • 27. Wang, Y.X., Markert, R., Xiang, J., et al: ‘Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system’, Mech. Syst. Signal Process. ., 2015, 60–61, pp. 243–251.
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