SOURCE-LINKED INTELLIGENCE

Longevity-focused health management of complex engineered systems

CORDIS · observation · Publication date unknown

itize immediate fault detection, they often overlook the interdependencies and evolving operational loads that influence asset lifespan. HEROES bridges these gaps by developing novel physics-informed machine learning (PIML) techniques to model long-term interdependent degradation and AI-driven, interpretable decision support tools. The framework features a module for modeling, understanding, and forecasting the health evolution of wind turbines and their components under dynamic operational conditions. By developing cutting-edge PIML methods, HEROES predicts degradation, assesses interdependencies, and evaluates lifetime consumption under varying operating conditions. The project leads the way in integrating and developing physics-informed graph neural networks (GNNs) with Neural ODEs, transformers, and hierarchical GNNs, enabling accurate long-term predictions and scalable modeling across components, systems, and fleets. Building on these predictive degradation models, HEROES proposes a pioneering decision support system for longevity optimization. It develops multi-agent reinforcem

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recordType

award

status

SIGNED

region

EU

value

1999759

unit

EUR