RT Conference Proceedings
T1 Monitoring Mooring Lines of Floating Offshore Wind Turbines: 												Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks
A1 Sharma, Smriti
A1 Nava, Vincenzo
AB This study introduces a pioneering monitoring system designed to mitigate operational costs and enhance the sustainability of Floating Offshore Wind Turbines (FOWT). The proposed framework combines Autoregressive models with a Stacked Auto-Associative-based Deep Neural Network (AANN-DNN) to detect and classify damages in mooring systems of FOWTs. By extracting damage-sensitive features (DSFs) using the AR models from time-series data and employing unsupervised learning in the auto-associative neural network, followed by supervised training with DNN, the approach demonstrates exceptional accuracy in damage identification and classification. Numerical simulations conducted using NREL’s OpenFAST software under diverse metocean conditions validate the method’s efficacy, offering a promising solution for efficient FOWT mooring line monitoring.
PB British Institute of Non-Destructive Testing
SN 9780903132848
YR 2024
FD 2024
LK https://hdl.handle.net/11556/4834
UL https://hdl.handle.net/11556/4834
LA eng
NO Sharma , S & Nava , V 2024 , Monitoring Mooring Lines of Floating Offshore Wind Turbines : Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks . in 20th International Conference on Condition Monitoring and Asset Management, CM 2024 . 20th International Conference on Condition Monitoring and Asset Management, CM 2024 , British Institute of Non-Destructive Testing , 20th International Conference on Condition Monitoring and Asset Management, CM 2024 , Oxford , United Kingdom , 18/06/24 . https://doi.org/10.1784/cm2024.3a2
NO conference
NO Publisher Copyright: © 2024 20th International Conference on Condition Monitoring and Asset Management, CM 2024. All rights reserved.
DS TECNALIA Publications
RD 28 sept 2024