Nicolás Gorostidi will defend his thesis on Monday, September 15th

The defence will take place at Sala Adela Moyua at the Faculty of Science and Technology of the EHU Leioa Campus at 11:00

Nicolás Gorostidi is a PhD candidate in mathematics and statistics, with a focus on applying deep learning to structural health monitoring of floating offshore wind turbine mooring systems. His research combines applied mathematics, computational mechanics, and statistical modeling to develop predictive maintenance strategies for offshore wind systems. He also hold an MSc in Computational Fluid Dynamics from Cranfield University, and a BSc in Mechanical Engineering from the University of Oviedo. Beyond academia, he is an experienced Jazz pianist, marathon runner, and baking enthusiast.

His thesis, titled “Structural Health Monitoring of Floating Offshore Wind Turbine Mooring Lines using Deep Learning” is supervised by Prof. David Pardo (BCAM, Ikerbasque & EHU) and Dr. Vincenzo Nava (Politecnico di Torino). It is scheduled to be defended in September 15th, 2025, at Sala Adela Moyua in the Faculty of Science and Technology, EHU Leioa Campus, at 11:00a.m.

On behalf of all members of BCAM, we would like to wish her all the best for the future, both professionally and personally.

Abstract

Floating offshore wind energy has strong potential as a clean energy source, but high operational costs and harsh environmental conditions limit its widespread deployment. Maintenance is often slowed by difficult weather, and conventional monitoring techniques provide unreliable assessments of turbine structural integrity. Structural health monitoring (SHM), an inverse problem from a mathematical perspective, offers a promising solution, using sensor data to infer system properties. This dissertation focuses on applying deep learning to SHM for floating turbines, specifically to detect degradation in mooring systems.

To address this problem, we develop deep learning models to identify two types of mooring damage: biological fouling and anchor displacements. Due to limited real-world data, we simulate turbine behavior using OpenFAST and extract meaningful statistical features from the platform’s responses to various operational conditions. Deterministic models, including multi-layer perceptrons and autoencoders, are trained to detect anomalies and estimate damage severity, providing early warnings for potential mooring failure, and enabling predictive maintenance strategies before performance losses occur.

We explore common sources of underlying uncertainty arising from structural design, measurement error, and long-term operation. We implement methods to alleviate the effects of these uncertainties, and quantify the individual and collective impact of each source of uncertainty in our predictions.

A probabilistic autoencoder is then implemented to quantify damage as a probability distribution, capturing cases with multiple plausible solutions. By comparing deterministic and probabilistic approaches, we demonstrate how uncertainty influences model predictions and quantify the confidence of our model’s estimations.