Data-Driven Damage Quantification of Floating Offshore Wind Turbine Platforms Based on Multi-Scale Encoder–Decoder with Self-Attention Mechanism

Data-Driven Damage Quantification of Floating Offshore Wind Turbine Platforms Based on Multi-Scale Encoder–Decoder with Self-Attention Mechanism

A Multi-Scale Convolutional Neural Network with Self Attention-based Auto Encoder–Decoder (MSCSA-AED), is a novel high-performance framework, presented here for the quantification of damage on a multibody floating offshore wind turbine (FOWT) structure. The model is equipped with similarity measurem...

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Bibliographic Details
Main Authors: Musa Bashir, Zifei Xu, Jin Wang, C. Guedes Soares
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Journal of Marine Science and Engineering
Subjects:
data-driven technique
damage quantification
floating offshore wind turbine
FOWT predictive maintenance
Convolutional Neural Network
multi-scale information fusion
Online Access:https://www.mdpi.com/2077-1312/10/12/1830

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