Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation-Reference-Cited by-同舟云学术

Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation

Published:2024-04 Issue: Volume:224 Page:120152
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Hang Xinyu^ORCID,Zhu Xiaoxun,Gao Xiaoxia^ORCID,Wang Yu,Liu Longhu

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference54 articles.

1. Fault diagnosis of gas turbines with thermodynamic analysis restraining the interference of boundary conditions based on STN;Zhou;Int. J. Mech. Sci.,2021

2. A review of non-destructive testing on wind turbines blades;Márquez;Renew. Energy,2020

3. Open hole tensile behavior of plain woven carbon/glass hybrid composites;Yu;Int. J. Mech. Sci.,2023

4. Costs of repair of wind turbine blades: influence of technology aspects;Mishnaevsky;Wind Energy,2020

5. Intelligent fault diagnosis of turbine blade cracks via multiscale sparse filtering and multi-kernel support vector machine for information fusion;Huang;Adv. Eng. Inf.,2023

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