IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2165-d1387314.html
   My bibliography  Save this article

Non-Contact Wind Turbine Blade Crack Detection Using Laser Doppler Vibrometers

Author

Listed:
  • Ali Zabihi

    (Department of Mechanical Engineering, Rowan University, Glassboro, NJ 08028, USA)

  • Farhood Aghdasi

    (Department of Mechanical Engineering, Rowan University, Glassboro, NJ 08028, USA)

  • Chadi Ellouzi

    (Department of Mechanical Engineering, Rowan University, Glassboro, NJ 08028, USA)

  • Nand Kishore Singh

    (Department of Mechanical Engineering, Rowan University, Glassboro, NJ 08028, USA)

  • Ratneshwar Jha

    (Department of Mechanical Engineering, Rowan University, Glassboro, NJ 08028, USA
    Aerospace, Physics, and Space Sciences, Florida Institute of Technology, Melbourne, FL 08028, USA)

  • Chen Shen

    (Department of Mechanical Engineering, Rowan University, Glassboro, NJ 08028, USA)

Abstract

In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite its significance, wind energy infrastructure is susceptible to damage from various factors including wind or sea waves, rapidly changing environmental conditions, delamination, crack formation, and structural deterioration over time. This research focuses on investigating non-destructive testing (NDT) of wind turbine blades (WTBs) using approaches based on the vibration of the structures. To this end, WTBs are first made from glass fiber-reinforcement polymer (GFRP) using composite molding techniques, and then a short pulse is generated in the structure by a piezoelectric actuator made from lead zirconate titanate (PZT-5H) to generate guided waves. A numerical approach is presented based on solving the elastic time-harmonic wave equations, and a laser Doppler vibrometer (LDV) is utilized to collect the vibrational data in a remote manner, thereby facilitating the crack detection of WTBs. Subsequently, the wave propagation characteristics of intact and damaged structures are analyzed using the Hilbert–Huang transformation (HHT) and fast Fourier transformation (FFT). The results reveal noteworthy distinctions in damaged structures, where the frequency domain exhibits additional components beyond those identified by FFT, and the time domain displays irregularities in proximity to the crack region, as detected by HHT. The results suggest a feasible approach to detecting potential cracks of WTBs in a non-contact and reliable way.

Suggested Citation

  • Ali Zabihi & Farhood Aghdasi & Chadi Ellouzi & Nand Kishore Singh & Ratneshwar Jha & Chen Shen, 2024. "Non-Contact Wind Turbine Blade Crack Detection Using Laser Doppler Vibrometers," Energies, MDPI, vol. 17(9), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2165-:d:1387314
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2165/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/9/2165/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bo Chen & Sheng-lin Zhao & Peng-yun Li, 2014. "Application of Hilbert-Huang Transform in Structural Health Monitoring: A State-of-the-Art Review," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-22, June.
    2. Amna Algolfat & Weizhuo Wang & Alhussein Albarbar, 2023. "The Sensitivity of 5MW Wind Turbine Blade Sections to the Existence of Damage," Energies, MDPI, vol. 16(3), pages 1-20, January.
    3. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    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. Tian, Runze & Kou, Peng & Zhang, Yuanhang & Mei, Mingyang & Zhang, Zhihao & Liang, Deliang, 2024. "Residual-connected physics-informed neural network for anti-noise wind field reconstruction," Applied Energy, Elsevier, vol. 357(C).
    2. Weiwu Feng & Da Yang & Wenxue Du & Qiang Li, 2023. "In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    3. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    4. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    5. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    6. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    7. Sun, Shilin & Li, Qi & Hu, Wenyang & Liang, Zhongchao & Wang, Tianyang & Chu, Fulei, 2023. "Wind turbine blade breakage detection based on environment-adapted contrastive learning," Renewable Energy, Elsevier, vol. 219(P2).
    8. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    9. Jinhua Zhang & Hui Li & Peng Cheng & Jie Yan, 2024. "Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network," Energies, MDPI, vol. 17(2), pages 1-16, January.
    10. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
    11. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).

    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:17:y:2024:i:9:p:2165-:d:1387314. 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.