Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO006: RUL-ET AI-powered data framework for predicting the RUL of offshore renewable mooring and cable systems
Miren Sánchez, Senior researcher in marine renewable energy, Tecnalia
Abstract
The RUL-ET project addresses a critical challenge in offshore renewable energy: how to accurately estimate the remaining useful life (RUL) of dynamic cables and mooring systems in floating platforms. These components are essential for the safe and efficient operation of offshore wind and marine energy devices, yet their degradation over time remains poorly characterized, leading to conservative maintenance and high operational costs. RUL-ET introduces a novel, digital-first methodology combining real-world, lab-based, and synthetic data to train AI models capable of diagnosing structural health and forecasting degradation. Through a consortium of leading research centers and industry stakeholders, RUL-ET develops new AI architectures, enriched datasets, and transferable prediction models that support data-driven decision-making in operation and maintenance (O&M). Key outcomes include: * A curated, multi-source data library (field, lab, simulated). * AI-based diagnosis and RUL prediction software. * Demonstrations in different operational contexts (e.g. mooring lines vs umbilical cables). * Support tools for predictive maintenance and uncertainty management. RUL-ET contributes to digital transformation in offshore renewables by enabling scalable, transferable, and robust data-based prognostics that improve safety, reduce interventions, extend component life, and lower the levelised cost of energy (LCOE). It exemplifies the power of AI-readiness and collaborative data engineering in enabling high-impact, FAIR-compliant innovation for the sector.
No recording available for this poster.
