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Longevity-focused health management of complex engineered systems

Project description

A long-term sustainable approach to asset management

As industries strive for sustainability, they shift from quick fixes to strategies that ensure the long-term health of critical assets. The ERC-funded HEROES project aims to transform how industries understand and manage asset degradation. Instead of treating faults as isolated events, HEROES views them as part of a larger, interconnected system affected by dynamic conditions. Researchers will leverage advanced physics-informed machine learning and AI tools to create models that predict how assets evolve over time. These insights will feed into decision-making systems designed to optimise maintenance and extend lifespans. Validated on wind turbines, HEROES will help optimise operation and maintenance strategies at component, system, and fleet levels, setting a new benchmark for sustainable and reliable asset management across industries.

Objective

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.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2025-COG

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Host institution

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 999 759,00
Address
BATIMENT CE 3316 STATION 1
1015 LAUSANNE
Switzerland

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Region
Schweiz/Suisse/Svizzera Région lémanique Vaud
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 999 759,00

Beneficiaries (1)

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