Longevity-focused health management of complex engineered systems

forthcoming

The HEROES project proposes a transformative framework for longevity-focused health management of complex assets, shifting the focus from short-term performance metrics to long-term sustainability, reliability, and efficiency. While traditional methods prioritize 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 reinforcement learning for health-aware control to extend component lifetimes, and optimize design for maintenance through operational feedback and generative models. HEROES further innovatively integrates counterfactual reasoning and neuro-symbolic AI to simulate long-term “what-if” scenarios, providing fully interpretable interaction with decision makers. Validated on wind turbines, HEROES aims to optimize operation and maintenance strategies at component, system, and fleet levels. Its methodological innovations will drive a paradigm shift towards longevity-focused health management, reduced environmental impact, and enhanced reliability.