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Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution

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  • Tang, Baoping
  • Liu, Wenyi
  • Song, Tao

Abstract

Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind turbine condition monitoring, in which the time-frequency analysis techniques such as Wigner-Ville distribution (WVD) are widely used. Theoretically WVD has an infinite resolution in time-frequency domain. For early wind turbine fault signals, however, there are two main difficulties in WVD analysis. One is strong noise signals in the background and the other is cross terms in WVD itself. In this paper, continuous wavelet transformation (CWT) is employed to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD. In the CWT de-noising process, the Morlet wavelet, whose shape is similar to mechanical shock signals, is chosen to perform CWT on the raw vibration signals. The appropriate scale parameter for CWT is optimized by the cross validation method (CVM). An ATW based on the Smoothed Pseudo Wigner-Ville distribution (SPWVD) spectrum is taken to be a window function to suppress the cross terms in WVD. The new method can not only remove cross terms faraway from the auto terms, but also keep high energy close to every instantaneous frequency, the virtues such as high time-frequency resolution, and good energy aggregation etc. The wind turbine gear fault diagnosis experiment results indicate that the proposed method has a good de-nosing performance and is effective in suppressing the cross terms and extracting fault feature.

Suggested Citation

  • Tang, Baoping & Liu, Wenyi & Song, Tao, 2010. "Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution," Renewable Energy, Elsevier, vol. 35(12), pages 2862-2866.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:12:p:2862-2866
    DOI: 10.1016/j.renene.2010.05.012
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    1. Liu, Wenyi & Tang, Baoping & Jiang, Yonghua, 2010. "Status and problems of wind turbine structural health monitoring techniques in China," Renewable Energy, Elsevier, vol. 35(7), pages 1414-1418.
    2. Dalili, N. & Edrisy, A. & Carriveau, R., 2009. "A review of surface engineering issues critical to wind turbine performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 428-438, February.
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    Cited by:

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    2. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    3. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
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    5. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    6. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    7. Yi, Cancan & Yu, Zhaohong & Lv, Yong & Xiao, Han, 2020. "Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis," Renewable Energy, Elsevier, vol. 161(C), pages 736-749.
    8. Li, Jimeng & Chen, Xuefeng & Du, Zhaohui & Fang, Zuowei & He, Zhengjia, 2013. "A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis," Renewable Energy, Elsevier, vol. 60(C), pages 7-19.
    9. Hui Li & Bangji Fan & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2020. "Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms," Energies, MDPI, vol. 13(6), pages 1-20, March.
    10. Ukashatu Abubakar & Saad Mekhilef & Hazlie Mokhlis & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski & Hussain Bassi & Muhyaddin Jamal Hosin Rawa, 2018. "Transient Faults in Wind Energy Conversion Systems: Analysis, Modelling Methodologies and Remedies," Energies, MDPI, vol. 11(9), pages 1-33, August.
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    12. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    13. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    14. Tengda Huang & Sheng Fu & Haonan Feng & Jiafeng Kuang, 2019. "Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention," Energies, MDPI, vol. 12(20), pages 1-19, October.
    15. Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    16. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    17. He, Guolin & Ding, Kang & Li, Weihua & Jiao, Xintao, 2016. "A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique," Renewable Energy, Elsevier, vol. 87(P1), pages 364-375.
    18. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    19. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    20. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    21. Majidi Nezhad, Meysam & Neshat, Mehdi & Piras, Giuseppe & Astiaso Garcia, Davide, 2022. "Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    22. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    23. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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