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The Use of Coherence Functions of Acoustic Emission Signals as a Method for Diagnosing Wind Turbine Blades

Author

Listed:
  • Artur Bejger

    (Faculty of Marine Engineering, Maritime University of Szczecin, 71-650 Szczecin, Poland)

  • Jan Bohdan Drzewieniecki

    (Faculty of Marine Engineering, Maritime University of Szczecin, 71-650 Szczecin, Poland)

  • Przemysław Bartoszko

    (Faculty of Marine Engineering, Maritime University of Szczecin, 71-650 Szczecin, Poland)

  • Ewelina Frank

    (Windhunter Academy, Windhunter Group, 75-221 Koszalin, Poland)

Abstract

Acoustic emission (AE) is one of the methods of non-destructive evaluation (NDE), and functions by means of detecting elastic waves caused by dynamic movements in AE sources, such as cracking in various material structures. In the case of offshore wind turbines, the most vulnerable components are their blades. Therefore, the authors proposed a method using AE to diagnose wind turbine blades. In the identification of their condition during monitoring, it was noted that the changes characterising blade damage involve non-linear phenomena; hence, wave phenomena do not occur in the principal components of the amplitudes or their harmonics. When the authors used the inverse transformation in the signal analysis process, which essentially leads to finding a signal measure, it allowed them to distinguish the wave spectrum of an undamaged system from one in which the material structure of the blade was damaged. The characteristic frequencies of individual phenomena interacting with the blade of a working turbine provide the basis for the introduction of filters (or narrowband sensors) that will increase the quality of the diagnosis itself. Considering the above, the use of the coherence function was proposed as an important measure of a diagnostic signal, reflecting a given condition of the blade.

Suggested Citation

  • Artur Bejger & Jan Bohdan Drzewieniecki & Przemysław Bartoszko & Ewelina Frank, 2023. "The Use of Coherence Functions of Acoustic Emission Signals as a Method for Diagnosing Wind Turbine Blades," Energies, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7474-:d:1275653
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    References listed on IDEAS

    as
    1. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    2. Guo, Jihong & Liu, Chao & Cao, Jinfeng & Jiang, Dongxiang, 2021. "Damage identification of wind turbine blades with deep convolutional neural networks," Renewable Energy, Elsevier, vol. 174(C), pages 122-133.
    3. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
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