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A review of wind turbine bearing condition monitoring: State of the art and challenges

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  • de Azevedo, Henrique Dias Machado
  • Araújo, Alex Maurício
  • Bouchonneau, Nadège

Abstract

Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.

Suggested Citation

  • de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
    • Handle: RePEc:eee:rensus:v:56:y:2016:i:c:p:368-379
    DOI: 10.1016/j.rser.2015.11.032
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    References listed on IDEAS

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    6. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    7. Ruiz de la Hermosa González-Carrato, Raúl, 2017. "Sound and vibration-based pattern recognition for wind turbines driving mechanisms," Renewable Energy, Elsevier, vol. 109(C), pages 262-274.
    8. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    9. Peyman Mazidi & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model," Journal of Risk and Reliability, , vol. 231(2), pages 121-129, April.
    10. Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho, 2018. "Prognostic techniques applied to maintenance of wind turbines: a concise and specific review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1917-1925.
    11. Xin, Ge & Hamzaoui, Nacer & Antoni, Jérôme, 2020. "Extraction of second-order cyclostationary sources by matching instantaneous power spectrum with stochastic model – application to wind turbine gearbox," Renewable Energy, Elsevier, vol. 147(P1), pages 1739-1758.
    12. Peyman Mazidi & Mian Du & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "A health condition model for wind turbine monitoring through neural networks and proportional hazard models," Journal of Risk and Reliability, , vol. 231(5), pages 481-494, October.
    13. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    14. Augusto Bianchini & Jessica Rossi & Lauro Antipodi, 2018. "A procedure for condition-based maintenance and diagnostics of submersible well pumps through vibration monitoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 999-1013, October.
    15. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.
    16. Nguyen, Thi-Anh-Tuyet & Chou, Shuo-Yan & Yu, Tiffany Hui-Kuang, 2022. "Developing an exhaustive optimal maintenance schedule for offshore wind turbines based on risk-assessment, technical factors and cost-effective evaluation," Energy, Elsevier, vol. 249(C).

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