Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection
Author:
Affiliation:
1. Engineering Department, Machine Learning and Drone Laboratory, Utah Valley University, Orem, UT, USA
Funder
Utah System of Higher Education (USHE)-Deep Technology Initiative
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10453577.pdf?arnumber=10453577
Reference165 articles.
1. A review of damage detection methods for wind turbine blades
2. Probabilistic analysis of wind turbine performance degradation due to blade erosion accounting for uncertainty of damage geometry
3. Analysis and Detection of Erosion in Wind Turbine Blades
4. Assessing wind turbine energy losses due to blade leading edge erosion cavities with parametric CAD and 3D CFD
5. A wind turbine blade leading edge rain erosion computational framework
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1. A Machine Vision Method for Identifying Blade Tip Clearance in Wind Turbines;Sensors;2024-09-13
2. Fluid-structure interaction and life prediction of small-scale damaged horizontal axis wind turbine blades;Results in Engineering;2024-09
3. Wind Turbine Blade Defect Detection Algorithm Based on Lightweight MES-YOLOv8n;IEEE Sensors Journal;2024-09-01
4. Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control;Journal of Marine Science and Engineering;2024-07-31
5. Identification and Localization of Wind Turbine Blade Faults Using Deep Learning;Applied Sciences;2024-07-19
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