RESEARCH PAPER
Detecting damages for wind turbine blades based on Chebyshev polynomial approximation and uniform load surface curvature
1
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China
2
Key Laboratory of Equipment Monitoring and Intelligent Operation and Maintenance under Extreme Working Conditions in the Province, Zhejiang, PR China
3
School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, P.R.China
These authors had equal contribution to this work
Submission date: 2024-12-24
Final revision date: 2025-02-11
Acceptance date: 2025-05-01
Online publication date: 2025-05-22
Publication date: 2025-05-22
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):204575
HIGHLIGHTS
- An reliable and effective method for detecting WT blade damage method is proposed.
- It reduces the requirements for the number of measurement points and modal order.
- The process is applied to NREL 5MW WT blade and achieved high-order accuracy result.
KEYWORDS
finite element methodwind turbine bladedamage detection methodChebyshev polynomial approximationuniform load surface curvature
TOPICS
ABSTRACT
Wind turbine blades are among the most critical components of a wind turbine. Cracking is the most prevalent type of the WT blades damage, making it essential to develop methods for early detection and precise assessment of crack locations and severity. This paper proposes a novel method based on uniform load surface (ULS) curvature variation for determining the damage location in wind turbine blades. The Chebyshev polynomial is utilized instead of the central difference method to calculate ULS curvature. This method only needs the first-order natural frequency of the WT blade to detect the damage of the WT blade, and the numerical simulation results show that its calculation accuracy has low requirements on the number of measurement points. An experimental platform was established to collect modal data and a model updating technique was employed to adjust the simulation model parameters. Consequently, this method enhances the traditional modal curvature approach, offering a more comprehensive and reliable technique for structural health monitoring of wind turbine blades.
ACKNOWLEDGEMENTS
The authors are grateful to the support of National Natural Science Foundation of China (No. 52005373 and 12202318), the Zhejiang Provincial Natural Science Foundation of China (LQ21E050002) ,Wenzhou Municipal Science and Technology Bureau, China (No. G2020014), and Innovation Project of Guangxi Graduate Education(YCBZ2023135).
REFERENCES (36)
1.
Tehrani K, Beikbabaei M, Mehrizi-Sani A, Jamshidi M. A smart multiphysics approach for wind turbines design in industry 5.0. Journal of Industrial Information Integration. 2024;42(0022460X). https://doi:10.1016/j.jii.2024....
2.
Shakya P, Thomas M, Seibi AC, Shekaramiz M, Masoum MS. Fluid-structure interaction and life prediction of small-scale damaged horizontal axis wind turbine blades. Results in Engineering. 2024;23. https://doi:10.1016/j.rineng.2....
3.
Fang H, Feng Y, Wei X, Xiong J. Wind turbine blade damage aerodynamic profile analysis and its repair techniques. Energy Reports. 2023;9:1-10. https://doi:10.1016/j.egyr.202....
4.
Kang S, He Y, Li W, Liu S. Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images. Computers, Materials & Continua. 2024;81(1):933-49. https://doi:10.32604/cmc.2024.....
5.
Oliveira MA, Simas Filho EF, Albuquerque MCS, Santos YTB, da Silva IC, Farias CTT. Ultrasound-based identification of damage in wind turbine blades using novelty detection. Ultrasonics. 2020;108. https://doi:10.1016/j.ultras.2....
6.
Rizk P, Rizk F, Karganroudi SS, Ilinca A, Younes R, Khoder J. Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection. Energy and AI. 2024;16. https://doi:10.1016/j.egyai.20....
7.
Wang B, Sun W, Wang H, Xu T, Zou Y. Research on rapid calculation method of wind turbine blade strain for digital twin. Renewable Energy. 2024;221. https://doi:10.1016/j.renene.2....
8.
Chen Y, Griffith DT. Experimental and numerical full-field displacement and strain characterization of wind turbine blade using a 3D Scanning Laser Doppler Vibrometer. Optics & Laser Technology. 2023;158. https://doi:10.1016/j.optlaste....
9.
Khazaee M, Derian P, Mouraud A. A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods. Renewable Energy. 2022;199:1568-79. https://doi:10.1016/j.renene.2....
10.
Song F, Han Y, William Heath A, Hou M. Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network. Engineering Failure Analysis. 2024;166. https://doi:10.1016/j.engfaila....
11.
Xu D, Liu PF, Chen ZP, Leng JX, Jiao L. Achieving robust damage mode identification of adhesive composite joints for wind turbine blade using acoustic emission and machine learning. Composite Structures. 2020;236. https://doi:10.1016/j.compstru....
12.
Ren C, Xing Y. An efficient active learning Kriging approach for expected fatigue damage assessment applied to wind turbine structures. Ocean Engineering. 2024;305. https://doi:10.1016/j.oceaneng....
13.
Zhang L, Wang X, Lu S, Jiang X, Ma C, Lin L, et al. Fatigue damage monitoring of repaired composite wind turbine blades using high-stability buckypaper sensors. Composites Science and Technology. 2022;227. https://doi:10.1016/j.compscit....
14.
Li J, Dao MH, Le QT. Data-driven modal parameterization for robust aerodynamic shape optimization of wind turbine blades. Renewable Energy. 2024;224. https://doi:10.1016/j.renene.2....
15.
Pacheco-Chérrez J, Cárdenas D, Delgado-Gutiérrez A, Probst O. Operational modal analysis for damage detection in a rotating wind turbine blade in the presence of measurement noise. Composite Structures. 2023;321. https://doi:10.1016/j.compstru....
16.
Song M, Partovi Mehr N, Moaveni B, Hines E, Ebrahimian H, Bajric A. One year monitoring of an offshore wind turbine: Variability of modal parameters to ambient and operational conditions. Engineering Structures. 2023;297. https://doi:10.1016/j.engstruc....
17.
Chen Y, Griffith DT. Blade mass imbalance identification and estimation for three-bladed wind turbine rotor based on modal analysis. Mechanical Systems and Signal Processing. 2023;197. https://doi:10.1016/j.ymssp.20....
18.
Dolinski L, Krawczuk M. Analysis of Modal Parameters Using a Statistical Approach for Condition Monitoring of the Wind Turbine Blade. Applied Sciences. 2020;10(17). https://doi:10.3390/app1017587....
19.
Selna LG, Shilling-Burg HT, JR., Kerr PA. Finite Element Analysis of Dental Structures - Axisymmetric and Plane Stress Idealizations. Journal of Biomedical Materials Research. 1975;9:237-52.
20.
Pai PF, G.Young L. Damage detection of beam using operational deflection shapes. International Journal of Soilds and Structures. 2001;38:3161-92.
21.
Viola E, Federici L, Nobile L. Detection of crack location using cracked beam element for structural analysis. Theoretical and Applied Fracture Mechanics. 2001;36:23-5.
22.
Rodríguez R, Escobar JA, Gómez R. Damage detection in instrumented structures without baseline modal parameters. Engineering Structures. 2010;32(6):1715-22. https://doi:10.1016/j.engstruc....
23.
Wei F, Pizhong Q. Vibration-based Damage Identification Methods: A Review and Comparative Study. Structural Health Monitoring. 2010;10(1):83-111. https://doi:10.1177/1475921710....
24.
Huang M, Li X, Lei Y, Gu J. Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization. Structures. 2020;28:1119-36. https://doi:10.1016/j.istruc.2....
25.
Wang N, Zhu R-h, Wang Q-m, Zheng J-h, Zhang J-b. A method for quantitative damage identification in a high-piled wharf based on modal strain energy residual variability. Ocean Engineering. 2022;254. https://doi:10.1016/j.oceaneng....
26.
Gao J-X, An Z-W, Ma Q, Bai X-Z. Residual strength assessment of wind turbine rotor blade composites under combined effects of natural aging and fatigue loads. Eksploatacja i Niezawodność – Maintenance and Reliability. 2020;22(4):601-9. https://doi:10.17531/ein.2020.....
27.
Kim D-G, Lee S-B. Structural damage identification of a cantilever beam using excitation force level control. Mechanical Systems and Signal Processing. 2010;24(6):1814-30. https://doi:10.1016/j.ymssp.20....
28.
Sung SH, Jung HJ, Jung HY. Damage detection for beam-like structures using the normalized curvature of a uniform load surface. Journal of Sound and Vibration. 2013;332(6):1501-19. https://doi:10.1016/j.jsv.2012....
29.
Wu D, Law SS. Damage localization in plate structures from uniform load surface curvature. Journal of Sound and Vibration. 2004;276(1-2):227-44. https://doi:10.1016/j.jsv.2003....
30.
Jiang Y, Sun J, Lin Q, Xiang J. A two-stage method to detect damages in aluminum plates using curvature modal shape subtraction indicator and particle swarm optimization. Thin-Walled Structures. 2023;185. https://doi:10.1016/j.tws.2023....
31.
Bai R, Wang H, Sun W, Shi Y. Fault diagnosis method for rotating machinery based on SEDenseNet and Gramian Angular Field. Eksploatacja i Niezawodność – Maintenance and Reliability. 2024;26(4). https://doi:10.17531/ein/19144....
32.
Wang Y, Liang M, Xiang J. Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information. Mechanical Systems and Signal Processing. 2014;48(1-2):351-67. https://doi:10.1016/j.ymssp.20....
33.
Cadoret A, Denimal-Goy E, Leroy J-M, Pfister J-L, Mevel L. Damage detection and localization method for wind turbine rotor based on Operational Modal Analysis and anisotropy tracking. Mechanical Systems and Signal Processing. 2025;224. https://doi:10.1016/j.ymssp.20....
34.
Jiang H, Jiang Y, Xiang J. Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades. Structural Health Monitoring. 2023;23(2):1025-36. https://doi:10.1177/1475921723....
35.
Pacheco-Chérrez J, Probst O. Vibration-based damage detection in a wind turbine blade through operational modal analysis under wind excitation. Materials Today: Proceedings. 2022;56:291-7. https://doi:10.1016/j.matpr.20....
36.
Yang C, Li P, Lang X, Wu J. A novel NMF-DiCCA deep learning method and its application in wind turbine blade icing failure identification. Eksploatacja i Niezawodność – Maintenance and Reliability. 2024;26(4). https://doi:10.17531/ein/19038....
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