IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2730-d1097697.html
   My bibliography  Save this article

Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring

Author

Listed:
  • Jersson X. Leon-Medina

    (Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain)

  • Francesc Pozo

    (Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
    Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain)

Abstract

In recent years, the scope of structural health monitoring in wind turbines has broadened due to the development of innovative data-driven methodologies [...]

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2730-:d:1097697
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2730/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/6/2730/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francesco Castellani & Luigi Garibaldi & Alessandro Paolo Daga & Davide Astolfi & Francesco Natili, 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements," Energies, MDPI, vol. 13(6), pages 1-18, March.
    2. Jichuan Kang & Zihao Wang & C. Guedes Soares, 2020. "Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine," Energies, MDPI, vol. 13(14), pages 1-17, July.
    3. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    4. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    5. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
    6. 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.
    7. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    8. Zuojun Liu & Cheng Xiao & Tieling Zhang & Xu Zhang, 2020. "Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning," Energies, MDPI, vol. 13(2), pages 1-21, January.
    9. 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.
    10. Hui Li & Bangji Fan & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2020. "Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms," Energies, MDPI, vol. 13(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    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. Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
    2. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    3. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.
    4. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    5. Ali Fayazi & Hossein Ghayoumi Zadeh & Hossein Ahmadian & Mahdi Ghane & Omid Rahmani Seryasat, 2024. "Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L 1 Adaptive Sliding Mode Control ( SMC ) Scheme," Energies, MDPI, vol. 17(16), pages 1-20, August.
    6. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    7. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
    8. 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.
    9. Xiange Tian & Yongjian Jiang & Chen Liang & Cong Liu & You Ying & Hua Wang & Dahai Zhang & Peng Qian, 2022. "A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network," Energies, MDPI, vol. 15(18), pages 1-15, September.
    10. 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.
    11. Mingzhu Tang & Zixin Liang & Huawei Wu & Zimin Wang, 2021. "Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF," Energies, MDPI, vol. 14(19), pages 1-13, October.
    12. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    13. Xiao Chen & Martin A. Eder & Asm Shihavuddin & Dan Zheng, 2021. "A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance," Sustainability, MDPI, vol. 13(2), pages 1-10, January.
    14. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    15. Jorge Maldonado-Correa & Marcelo Valdiviezo-Condolo & Estefanía Artigao & Sergio Martín-Martínez & Emilio Gómez-Lázaro, 2024. "Classification of Highly Imbalanced Supervisory Control and Data Acquisition Data for Fault Detection of Wind Turbine Generators," Energies, MDPI, vol. 17(7), pages 1-20, March.
    16. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    17. Jong-Yih Kuo & Shang-Yi You & Hui-Chi Lin & Chao-Yang Hsu & Baiying Lei, 2022. "Constructing Condition Monitoring Model of Wind Turbine Blades," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    18. Tobias Mueller & Steven Gronau, 2023. "Fostering Macroeconomic Research on Hydrogen-Powered Aviation: A Systematic Literature Review on General Equilibrium Models," Energies, MDPI, vol. 16(3), pages 1-33, February.
    19. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    20. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.

    More about this item

    Keywords

    n/a;

    Statistics

    Access and download statistics

    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:gam:jeners:v:16:y:2023:i:6:p:2730-:d:1097697. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.