The Surface Damage Identifications of Wind Turbine Blades Based on ResNet50 Algorithm

Author(s):  
Peng Yang ◽  
Chaoyi Dong ◽  
Xiaoyi Zhao ◽  
Xiaoyan Chen
Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 676 ◽  
Author(s):  
ASM Shihavuddin ◽  
Xiao Chen ◽  
Vladimir Fedorov ◽  
Anders Nymark Christensen ◽  
Nicolai Andre Brogaard Riis ◽  
...  

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision.


2009 ◽  
Vol 129 (5) ◽  
pp. 689-695
Author(s):  
Masayuki Minowa ◽  
Shinichi Sumi ◽  
Masayasu Minami ◽  
Kenji Horii

2021 ◽  
Author(s):  
Aileen G. Bowen Perez ◽  
Giovanni Zucco ◽  
Paul Weaver

Author(s):  
Salete Alves ◽  
Luiz Guilherme Vieira Meira de Souza ◽  
Edália Azevedo de Faria ◽  
Maria Thereza dos Santos Silva ◽  
Ranaildo Silva

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.


Author(s):  
Sri Sai P. Reddy ◽  
Rohan. Suresh ◽  
Hanamantraygouda. M.B. ◽  
B.P. Shivakumar

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