| Autor: |
FELIPE GONZÁLEZ, ÁNGEL ENCALADA-DÁVILA, CHRISTIAN TUTIVÉN, BRYAN PURUNCAJAS, YOLANDA VIDAL, CARLOS BENALCÁZAR-PARRA |
| Rok vydání: |
2022 |
| Zdroj: |
Proceedings of the 13th International Workshop on Structural Health Monitoring. |
| DOI: |
10.12783/shm2021/36264 |
| Popis: |
This work addresses the problem of damage detection on offshore wind turbine jacket-type foundations based on deep learning algorithms. The work utilizes data obtained from the vibration response of a lab-scale wind turbine foundation. The main contributions of this manuscript to damage detection are: (i) an autoencoder neural network trained with only healthy data drawing a normality model, and (ii) a threshold in the function of prediction errors to define the bound limits of damage. The methodology is evaluated using real vibration data from the lab-scale wind turbine foundation tagged with different noise levels and damage scenarios. The results of damage detection show a 100% accuracy, demonstrating that the proposed methodology is practical and promising to be employed in this kind of challenges. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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