IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v146y2020icp99-110.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119309334
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.06.094?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    2. Igliński, Bartłomiej & Iglińska, Anna & Koziński, Grzegorz & Skrzatek, Mateusz & Buczkowski, Roman, 2016. "Wind energy in Poland – History, current state, surveys, Renewable Energy Sources Act, SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 19-33.
    3. Kamila Pronińska & Krzysztof Księżopolski, 2021. "Baltic Offshore Wind Energy Development—Poland’s Public Policy Tools Analysis and the Geostrategic Implications," Energies, MDPI, vol. 14(16), pages 1-17, August.
    4. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.
    5. Katarzyna Chudy-Laskowska & Tomasz Pisula & Mirosław Liana & László Vasa, 2020. "Taxonomic Analysis of the Diversity in the Level of Wind Energy Development in European Union Countries," Energies, MDPI, vol. 13(17), pages 1-21, August.
    6. Kong, Decheng & Wang, Yunfeng & Li, Ming & Liang, Jingkang, 2022. "Experimental investigation of a novel hybrid drying system powered by a solar photovoltaic/thermal air collector and wind turbine," Renewable Energy, Elsevier, vol. 194(C), pages 705-718.
    7. Miriam Benedetti & Daniele Dadi & Lorena Giordano & Vito Introna & Pasquale Eduardo Lapenna & Annalisa Santolamazza, 2021. "Design of a Database of Case Studies and Technologies to Increase the Diffusion of Low-Temperature Waste Heat Recovery in the Industrial Sector," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    8. Ewa Chomać-Pierzecka & Anna Sobczak & Dariusz Soboń, 2022. "Wind Energy Market in Poland in the Background of the Baltic Sea Bordering Countries in the Era of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, March.
    9. Bodha, Venugopal Reddy & Srujana, A. & Chandrashekar, O., 2018. "A modified H-bridge voltage source converter with Fault Ride Capability," Energy, Elsevier, vol. 165(PB), pages 1380-1391.
    10. Tembhare, Saurabh P. & Barai, Divya P. & Bhanvase, Bharat A., 2022. "Performance evaluation of nanofluids in solar thermal and solar photovoltaic systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    11. Wang, Yibing & Xu, Yuanming & Lei, Yuyong, 2018. "An effect assessment and prediction method of ultrasonic de-icing for composite wind turbine blades," Renewable Energy, Elsevier, vol. 118(C), pages 1015-1023.
    12. Zhaolin Jia & Han Wu & Hao Chen & Wei Li & Xinyi Li & Jijian Lian & Shuaiqi He & Xiaoxu Zhang & Qixiang Zhao, 2022. "Hydrodynamic Response and Tension Leg Failure Performance Analysis of Floating Offshore Wind Turbine with Inclined Tension Legs," Energies, MDPI, vol. 15(22), pages 1-16, November.
    13. Claire Burch & Rebecca Loraamm & Travis Gliedt, 2020. "The “Green on Green” Conflict in Wind Energy Development: A Case Study of Environmentally Conscious Individuals in Oklahoma, USA," Sustainability, MDPI, vol. 12(19), pages 1-22, October.
    14. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    15. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    16. Ávila, Leandro & Mine, Miriam R.M & Kaviski, Eloy & Detzel, Daniel H.M., 2021. "Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic streamflow and wind speed time series: A Brazilian case study," Renewable Energy, Elsevier, vol. 167(C), pages 685-699.
    17. Dragomir, George & Șerban, Alexandru & Năstase, Gabriel & Brezeanu, Alin Ionuț, 2016. "Wind energy in Romania: A review from 2009 to 2016," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 129-143.
    18. Anthony Roy & François Auger & Florian Dupriez-Robin & Salvy Bourguet & Quoc Tuan Tran, 2018. "Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies," Energies, MDPI, vol. 11(7), pages 1-27, July.
    19. Liew, Peng Yen & Theo, Wai Lip & Wan Alwi, Sharifah Rafidah & Lim, Jeng Shiun & Abdul Manan, Zainuddin & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev, 2017. "Total Site Heat Integration planning and design for industrial, urban and renewable systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 964-985.
    20. Pan, Pengcheng & Sun, Yuwei & Yuan, Chengqing & Yan, Xinping & Tang, Xujing, 2021. "Research progress on ship power systems integrated with new energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:146:y:2020:i:c:p:99-110. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.