Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning

•A semi-supervised approach is proposed for detecting damage in mooring systems with limited labelled data.•A well-structured latent representation enhances both damage detection and data generation ability.•The performance surpasses that of other deep learning models when working with limited label...

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Veröffentlicht in:	Ocean engineering Jg. 330; S. 121199
Hauptverfasser:	Tamuly, Pranjal, Sharma, Smriti, Nava, Vincenzo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 30.06.2025
Schlagworte:	Damage diagnosis Floating wind turbines Mooring systems Offshore structures Semi-supervised learning Variational autoencoder Variational autoencoder Damage diagnosis Mooring systems Floating wind turbines Semi-supervised learning Offshore structures
ISSN:	0029-8018
Online-Zugang:	Volltext
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