[3A2] Monitoring mooring lines of floating offshore wind turbines: autoregressive coefficients and stacked auto-associative deep neural networks
S Sharma¹ and V Nava¹,²
¹Basque Center for Applied Mathematics, Spain
²Tecnalia, Spain
This study introduces a pioneering monitoring system designed to mitigate operational costs and enhance the sustainability of floating offshore wind turbines (FOWTs). 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 the National Renewable Energy Laboratory (NREL)’s OpenFAST software under diverse metocean conditions validate the method’s efficacy, offering a promising solution for efficient FOWT mooring line monitoring.
Keywords: structural health monitoring (SHM), offshore structures, damage diagnosis, mooring lines, autoregressive model (AR), auto-associative neural network (AANN), deep neural network (DNN).
Keywords: structural health monitoring (SHM), offshore structures, damage diagnosis, mooring lines, autoregressive model (AR), auto-associative neural network (AANN), deep neural network (DNN).
