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Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Author

Listed:
  • Rogerio Adriano da Fonseca Santiago

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Natasha Benjamim Barbosa

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Henrique Gomes Mergulhão

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Tassio Farias de Carvalho

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Alex Alisson Bandeira Santos

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil
    Instituto de Ciência, Inovação e Tecnologia em Energias Renováveis do Estado da Bahia—INCITERE, Salvador 40210-910, BA, Brazil)

  • Ricardo Cerqueira Medrado

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Jose Bione de Melo Filho

    (Eletrobras Chesf, R. Delmiro Gouveia, 333, Recife 41650-010, BA, Brazil)

  • Oberdan Rocha Pinheiro

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Erick Giovani Sperandio Nascimento

    (Surrey Institute for People-Centred AI, School of Computer Science and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
    Stricto Sensu Department, SENAI CIMATEC, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

Abstract

Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.

Suggested Citation

  • Rogerio Adriano da Fonseca Santiago & Natasha Benjamim Barbosa & Henrique Gomes Mergulhão & Tassio Farias de Carvalho & Alex Alisson Bandeira Santos & Ricardo Cerqueira Medrado & Jose Bione de Melo Fi, 2024. "Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis," Energies, MDPI, vol. 17(5), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1010-:d:1342907
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    References listed on IDEAS

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    1. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    3. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
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    Citations

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    Cited by:

    1. Cuesta, Jokin & Leturiondo, Urko & Vidal, Yolanda & Pozo, Francesc, 2025. "A review of prognostics and health management techniques in wind energy," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    2. Dela Peña, Arthur C. & Rutao, Meredith I., 2026. "Task-level causal effects of maintenance staffing shortages on aircraft-on-ground (AOG) and reliability," Research in Transportation Economics, Elsevier, vol. 115(C).
    3. Shadi, Mohammad Reza & Mirshekali, Hamid & Shaker, Hamid Reza, 2025. "Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    4. Li, Mingxin & Xu, Zifei & Li, Shen & Kikuchi, Yuka & Dong, You & Gryllias, Konstantinos C. & Baraldi, Piero & Zio, Enrico & Carroll, James, 2026. "Health prognostics and maintenance decision-making for wind energy: A comprehensive overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PA).

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