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Publications
Bashir, M., Xu, ZF., Wang, J. and Guedes Soares, C. (2022), “Data-driven Damage Quantification of Floating Offshore Wind Turbine Platforms based on Multi-Scale Encoder-Decoder with Self-Attention Mechanism”, Journal of Marine Science and Engineering, Vol. 10, 1830.
A 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 measurement to enhance its capability to accurately quantify damage effects from different scales of coded features using raw platform responses and without human intervention. Case studies using different damage magnitudes on tendons of a 10 MW multibody FOWT have been used to examine the accuracy and reliability of the proposed model. The results show that addition of Square Euclidean (SE) distance enhances the MSCSA-AED model’s capability to suitably estimate the damage in structures operating in complex environments using only raw responses. Comparison of the model’s performance with other variants (DCN-AED and MSCNN-AED) used in the industry to extract the coded features from FOWT responses further demonstrates the superiority of MSCSA-AED in complex operating conditions, especially in low magnitude damage quantification - being the hardest to quantify. ; damage quantification; Floating offshore wind turbine; FOWT predictive maintenance; Convolutional Neural Network; Multi-scale information fusion.
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