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Fault Diagnosis of Wind Turbine Bearings Based on CEEMDAN-GWO-KELM

Author

Listed:
  • Liping Liu

    (College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
    College of Mechanical and Energy Engineering, Shanghai Technical Institute of Electronics & Information, Shanghai 201411, China)

  • Ying Wei

    (College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China)

  • Xiuyun Song

    (Faculty of International Languages, Qinggong College, North China University of Science and Technology, Tangshan 064000, China)

  • Lei Zhang

    (College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063009, China)

Abstract

To solve the problem of fault signals of wind turbine bearings being weak, not easy to extract, and difficult to identify, this paper proposes a fault diagnosis method for fan bearings based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Algorithm Optimization Kernel Extreme Learning Machine (GWO-KELM). First, eliminating the interference of noise on the collected vibration signal should be conducted, in which the wavelet threshold denoising approach is used in order to reduce the noise interference with the vibration signal. Next, CEEMDAN is used to decompose the signal after a denoising operation to obtain the multi-group intrinsic mode function (IMF), and the feature vector is selected by combining the correlation coefficients to eliminate the spurious feature components. Finally, the fuzzy entropy for the chosen IMF component is input into the GWO-KELM model as a feature vector for defect detection. After diagnosing the Case Western Reserve University (CWRU) dataset by the method presented in this research, it is found that the method can identify 99.42% of the various bearing states. When compared to existing combination approaches, the proposed method is shown to be more efficient for diagnosing wind turbine bearing faults.

Suggested Citation

  • Liping Liu & Ying Wei & Xiuyun Song & Lei Zhang, 2022. "Fault Diagnosis of Wind Turbine Bearings Based on CEEMDAN-GWO-KELM," Energies, MDPI, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:48-:d:1009719
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    References listed on IDEAS

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    1. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    2. Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
    3. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
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