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A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

Author

Listed:
  • Maryna Garan

    (Faculty of Mechanical Engineering, Department of Manufacturing Systems and Automation, Technical University of Liberec, 46117 Liberec, Czech Republic)

  • Khaoula Tidriri

    (CNRS (Centre National de la Recherche Scientifique), Grenoble INP (Institut Polytechnique), GIPSA-Lab (Grenoble Images Parole Signal Automatique), Université Grenoble Alpes, 38000 Grenoble, France)

  • Iaroslav Kovalenko

    (Faculty of Mechanical Engineering, Department of Manufacturing Systems and Automation, Technical University of Liberec, 46117 Liberec, Czech Republic)

Abstract

Nowadays, the energy sector is experiencing a profound transition. Among all renewable energy sources, wind energy is the most developed technology across the world. To ensure the profitability of wind turbines, it is essential to develop predictive maintenance strategies that will optimize energy production while preventing unexpected downtimes. With the huge amount of data collected every day, machine learning is seen as a key enabling approach for predictive maintenance of wind turbines. However, most of the effort is put into the optimization of the model architectures and its parameters, whereas data-related aspects are often neglected. The goal of this paper is to contribute to a better understanding of wind turbines through a data-centric machine learning methodology. In particular, we focus on the optimization of data preprocessing and feature selection steps of the machine learning pipeline. The proposed methodology is used to detect failures affecting five components on a wind farm composed of five turbines. Despite the simplicity of the used machine learning model (a decision tree), the methodology outperformed model-centric approach by improving the prediction of the remaining useful life of the wind farm, making it more reliable and contributing to the global efforts towards tackling climate change.

Suggested Citation

  • Maryna Garan & Khaoula Tidriri & Iaroslav Kovalenko, 2022. "A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines," Energies, MDPI, vol. 15(3), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:826-:d:731879
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    References listed on IDEAS

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    1. Wang, Jinjiang & Liang, Yuanyuan & Zheng, Yinghao & Gao, Robert X. & Zhang, Fengli, 2020. "An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples," Renewable Energy, Elsevier, vol. 145(C), pages 642-650.
    2. 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.
    3. Florian, Eleonora & Sgarbossa, Fabio & Zennaro, Ilenia, 2021. "Machine learning-based predictive maintenance: A cost-oriented model for implementation," International Journal of Production Economics, Elsevier, vol. 236(C).
    4. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
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    Cited by:

    1. Jersson X. Leon-Medina & Francesc Pozo, 2023. "Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring," Energies, MDPI, vol. 16(6), pages 1-4, March.
    2. Victoria Yildirir & Eugen Rusu & Florin Onea, 2022. "Wind Energy Assessments in the Northern Romanian Coastal Environment Based on 20 Years of Data Coming from Different Sources," Sustainability, MDPI, vol. 14(7), pages 1-21, April.

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