Defect detection of the surface of wind turbine blades combining attention mechanism

Author:

Liu Yu-hang,Zheng Yu-qiaoORCID,Shao Zhu-feng,Wei Tai,Cui Tian-cai,Xu Rong

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference56 articles.

1. AI-enabled and multimodal data driven smart health monitoring of wind power systems: a case research;Zhao;Adv. Eng. Inf.,2023

2. Analysis of effect of layup material layer thickness on low-order model frequency of wind turbine blades;Zheng;Acta Energ. Sol. Sin.,2022

3. A Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine blades;Jaramillo;Mech. Syst. Sig. Process.,2022

4. EDRNet: encoder-decoder residual network for salient object detection of strip steel surface defects;Song;IEEE Trans. Instrum. Meas.,2020

5. A review of impact loads on composite wind turbine blades: impact threats and classification;Verma;Renew. Sustain. Energy Rev.,2023

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