Vision-Based Measurement of Breathing Deformation in Wind Turbine Blade Fatigue Test

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade's hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade's dynamic responses in both flapwise and edgewise directions. A dedicated hardware-software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems.

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Publication Details

Journal
Journal of Imaging
Published
2026-04-17
DOI
https://doi.org/10.3390/jimaging12040174
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Vision-Based Measurement of Breathing Deformation in Wind Turbine Blade Fatigue Test

赵海 Zhao Hai, Leian Zhang, Cailin Li, Xianlong Wei et al.
Journal of Imaging
Structural Health Monitoring Techniques
article

Vision-Based Measurement of Breathing Deformation in Wind Turbine Blade Fatigue Test

赵海 Zhao Hai, Leian Zhang, Cailin Li, Xianlong Wei, Jinghua Wang, Zhiyong Wang
article en

Abstract

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade's hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade's dynamic responses in both flapwise and edgewise directions. A dedicated hardware-software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems.

Journal of ImagingVol. 12(4)
Shandong University of Technology (CN)
Natural Science Foundation of Shandong Province
Affordable and clean energy
Openalex Percentile: Top 21%
Structural Health Monitoring Techniques
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