Evidence map›Paper›PMID 42042517›Full record

ArticleJournal of imaging2026

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

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

Abstract read
In one paragraph

Article in Journal of imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xianlong WeiShandong Key Laboratory of Wind Power Equipment Testing, Evaluation and Service Support Technology, Shandong University of Technology, Zibo 255000, China.
Cailin LiShandong Key Laboratory of Wind Power Equipment Testing, Evaluation and Service Support Technology, Shandong University of Technology, Zibo 255000, China.ORCID 0000-0001-6275-9475
Zhiyong WangShandong Key Laboratory of Wind Power Equipment Testing, Evaluation and Service Support Technology, Shandong University of Technology, Zibo 255000, China.
Zhao HaiShandong Key Laboratory of Wind Power Equipment Testing, Evaluation and Service Support Technology, Shandong University of Technology, Zibo 255000, China.ORCID 0009-0005-4644-5923
Jinghua WangShandong Key Laboratory of Wind Power Equipment Testing, Evaluation and Service Support Technology, Shandong University of Technology, Zibo 255000, China.
Leian ZhangShandong Key Laboratory of Wind Power Equipment Testing, Evaluation and Service Support Technology, Shandong University of Technology, Zibo 255000, China.

Funding

Natural Science Foundation of Shandong Province No. ZR2024ME003Open Fund of State Key Laboratory of Offshore Wind Power Equipment and Wind Energy Efficient Utilization No. HFQZS2025-14
6 · The paper itself

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.

Indexed as

fatigue testoptical deformation measurementphotogrammetryvision-based sensingwind turbine blade

Identifiers

PMID42042517
PMCPMC13117450

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.