Portable motorized telescope system for wind turbine blades damage detection
Abstract 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 ar...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.12618 |
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| _version_ | 1832576595675578368 |
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| author | Alejandro Carnero Cristian Martín Manuel Díaz |
| author_facet | Alejandro Carnero Cristian Martín Manuel Díaz |
| author_sort | Alejandro Carnero |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-0c3bd06b5fdf42928843341b13b480cc |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-0c3bd06b5fdf42928843341b13b480cc2025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.12618Portable motorized telescope system for wind turbine blades damage detectionAlejandro Carnero0Cristian Martín1Manuel Díaz2ITIS Software Institute University of Málaga Málaga SpainITIS Software Institute University of Málaga Málaga SpainITIS Software Institute University of Málaga Málaga SpainAbstract 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.https://doi.org/10.1002/eng2.12618wind turbinesdamage detectionmotorized telescopemachine learning |
| spellingShingle | Alejandro Carnero Cristian Martín Manuel Díaz Portable motorized telescope system for wind turbine blades damage detection Engineering Reports wind turbines damage detection motorized telescope machine learning |
| title | Portable motorized telescope system for wind turbine blades damage detection |
| title_full | Portable motorized telescope system for wind turbine blades damage detection |
| title_fullStr | Portable motorized telescope system for wind turbine blades damage detection |
| title_full_unstemmed | Portable motorized telescope system for wind turbine blades damage detection |
| title_short | Portable motorized telescope system for wind turbine blades damage detection |
| title_sort | portable motorized telescope system for wind turbine blades damage detection |
| topic | wind turbines damage detection motorized telescope machine learning |
| url | https://doi.org/10.1002/eng2.12618 |
| work_keys_str_mv | AT alejandrocarnero portablemotorizedtelescopesystemforwindturbinebladesdamagedetection AT cristianmartin portablemotorizedtelescopesystemforwindturbinebladesdamagedetection AT manueldiaz portablemotorizedtelescopesystemforwindturbinebladesdamagedetection |