RT Journal Article
T1 Portable motorized telescope system for wind turbine blades damage detection
A1 Carnero, Alejandro
A1 Martín-Fernández, Cristian
A1 Díaz-Rodríguez, Manuel
K1 Turbinas eólicas
AB Wind turbines are among the fastest-growing sources of energy production and the maintenance operations include regular inspection of their blades, causing considerable downtime and cost. In addition, the manual inspection process involves a great risk. To address this challenge, in this article a preventive maintenance system for wind turbines based on deep computational learning techniques is presented. This open-source project aims to detect and classify possible surface damages on wind turbine blades to facilitate and improve the inspection of such infrastructures. The system consists of a stand-alone Android application that makes use of convolutional neural networks for image processing, a portable telescope to take precise photographs of the turbine blades, and a motorized mount that allows the movement of the telescope. The application tries to carry out a complete sweep of the surface of the wind turbine blades in an autonomous way based on the predictions of neural network models and finally presents the defects found to the user. Thanks to this, maintenance time would be reduced and the risk of manual intervention would be avoided. Accuracies of around 97% for label predictions and 90% for bounding box coordinate predictions have been achieved on the validation dataset. The proposed low-cost inspection system for detecting surface damages on blades has been experimentally validated in a real wind farm.
PB Wiley
YR 2023
FD 2023
LK https://hdl.handle.net/10630/33360
UL https://hdl.handle.net/10630/33360
LA eng
NO Carnero, Alejandro, Cristian Martín, and Manuel Díaz. "Portable motorized telescope system for wind turbine blades damage detection." Engineering Reports (2023): e12618.
NO 5G+TACTILE: Digital vertical twins for B5G/6G networks, Grant/Award Number: TSI-063000-2021-116; IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration, Grant/Award Number: PY20_00788; rFOG: Improving Latency and Reliability of Offloaded Computation to the FOG for Critical Services, Grant/Award Number: RT2018-099777-B-100; Wind Turbine Preventive Maintenance based on Deep Learning Techniques in the Fog, Grant/Award Number: UMA-CEIATECH-19
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026