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Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method

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  • Liu, Zepeng
  • Zhang, Long
  • Carrasco, Joaquin

Abstract

Blade bearings, also termed pitch bearings, are joint components of wind turbines, which can slowly pitch blades at desired angles to optimize electrical energy output. The failure of blade bearings can heavily reduce energy production, so blade bearing fault diagnosis is vitally important to prevent costly repair and unexpected failure. However, the main difficulties in diagnosing low-speed blade bearings are that the weak fault vibration signals are masked by many noise disturbances and the effective vibration data is very limited. To address these problems, this paper firstly deals with a naturally damaged large-scale and low-speed blade bearing which was in operation on a wind farm for over 15 years. Two case studies are conducted to collect the vibration data under the manual rotation condition and the motor driving condition. Then, a method called the empirical wavelet thresholding is applied to remove heavy noise and extract weak fault signals. The diagnostic results show that the proposed method can be an effective tool to diagnose naturally damaged large-scale wind turbine blade bearings.

Suggested Citation

  • Liu, Zepeng & Zhang, Long & Carrasco, Joaquin, 2020. "Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method," Renewable Energy, Elsevier, vol. 146(C), pages 99-110.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:99-110
    DOI: 10.1016/j.renene.2019.06.094
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    References listed on IDEAS

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    1. Kumar, Yogesh & Ringenberg, Jordan & Depuru, Soma Shekara & Devabhaktuni, Vijay K. & Lee, Jin Woo & Nikolaidis, Efstratios & Andersen, Brett & Afjeh, Abdollah, 2016. "Wind energy: Trends and enabling technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 209-224.
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    Cited by:

    1. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    2. Zhang, Liangwei & Lin, Jing & Shao, Haidong & Zhang, Zhicong & Yan, Xiaohui & Long, Jianyu, 2021. "End-to-end unsupervised fault detection using a flow-based model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    4. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    5. Thiyagarajan Rameshkumar & Perumal Chandrasekar & Raju Kannadasan & Venkatraman Thiyagarajan & Mohammed H. Alsharif & James Hyungkwan Kim, 2022. "Electrical and Mechanical Characteristics Assessment of Wind Turbine System Employing Acoustic Sensors and Matrix Converter," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    6. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.

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