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

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dc.contributor.authorSharma, Smriti
dc.contributor.authorNava, Vincenzo
dc.contributor.institutionRENOVABLES OFFSHORE
dc.date.accessioned2024-09-06T09:30:04Z
dc.date.available2024-09-06T09:30:04Z
dc.date.issued2024
dc.descriptionPublisher Copyright: © 2024 20th International Conference on Condition Monitoring and Asset Management, CM 2024. All rights reserved.
dc.description.abstractThis 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.en
dc.description.statusPeer reviewed
dc.identifier.citationSharma , 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
dc.identifier.citationconference
dc.identifier.doi10.1784/cm2024.3a2
dc.identifier.isbn9780903132848
dc.identifier.urihttps://hdl.handle.net/11556/4834
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85199446989&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherBritish Institute of Non-Destructive Testing
dc.relation.ispartof20th International Conference on Condition Monitoring and Asset Management, CM 2024
dc.relation.ispartofseries20th International Conference on Condition Monitoring and Asset Management, CM 2024
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsAuto Regressive Model (AR)
dc.subject.keywordsAuto-Associative Neural Network (AANN)
dc.subject.keywordsDamage diagnosis
dc.subject.keywordsDeep Neural Network (DNN)
dc.subject.keywordsMooring lines
dc.subject.keywordsOffshore Structures
dc.subject.keywordsStructural health monitoring (SHM)
dc.subject.keywordsIndustrial and Manufacturing Engineering
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsSafety, Risk, Reliability and Quality
dc.titleMonitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networksen
dc.typeconference output
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