Monitoring Mooring Lines of Floating Offshore Wind Turbines: 
						
						Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks

Sharma, Smriti; Nava, Vincenzo

Monitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks

Identifiers

URI: https://hdl.handle.net/11556/4834

ISBN: 9780903132848

DOI: 10.1784/cm2024.3a2

Publication date

2024

Authors

Sharma, Smriti

Nava, Vincenzo

Publisher

British Institute of Non-Destructive Testing

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Abstract

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.

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Keywords

Auto Regressive Model (AR) , Auto-Associative Neural Network (AANN) , Damage diagnosis , Deep Neural Network (DNN) , Mooring lines , Offshore Structures , Structural health monitoring (SHM) , Industrial and Manufacturing Engineering , Mechanical Engineering , Safety, Risk, Reliability and Quality

Type

conference output

Citation

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
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