Panel sessions

Moderators: Mayank Jha (U.Lorraine) and Olga Fink (EPFL)

• Christophe Bérenguer, Université Grenoble-Alps (GIPSA)
• Didier Theillol, Université de Lorraine
• Zhiguo Zeng, Centrale-Supélec (Saclay)
• Peter Fogh Odgard, Goldwin Energy
• Mihaela Mitici, TU Delft
• Rémi Bonidal, Arcelor Mittal

Abstract:

Nearly all mission- and safety-critical systems—energy and water infrastructure, process plants, transportation and autonomous vehicles, aerospace platforms—operate in closed loop under uncertainty and progressive wear. Controllers must remain resilient under abrupt faults and gradual degradations, often with incomplete physics and imperfect degradation models.

In this context, Health-Aware Control (HAC) is an emerging paradigm that explicitly or implicitly embeds prognostic information—such as state of health (SoH), reliability estimates, and remaining useful life (RUL)—into feedback control synthesis and reconfiguration.

Unlike traditional Prognostics and Health Management (PHM), which typically reasons in open loop, HAC reasons in closed loop: degradation dynamics are modeled (or learned) as part of the plant, and control laws are designed to optimize not only short-horizon performance but also long-horizon availability, safety, and asset lifespan

• Map the state-of-the-art vs. state-of-practice in HAC across sectors.
• Clarify interfaces among diagnosis, prognosis (RUL),and closed-loop control.
• Contrast HAC with Fault-Tolerant Control (FTC) and define complementarity with FDI.
• Examine roles of AI/ML (incl. Safe RL), digital twins, and physics-informed models.

Moderator: Nenad Mijatovic (Alstom)

• Jonathan Sprauel (Thales-Alenia Space)
• Ayoub Drissi (SNCF -Réseau)
• Diego Galar (Lulea University of Technology)

Abstract:

Prognostics and Health Management (PHM) is experiencing a transformative shift toward more reliable, explainable, and trustworthy data-driven approaches, such as Data Science and Artificial Intelligence (AI). This panel brings together industry leaders from transportation, railway, healthcare, finance, and other critical sectors, as well as academic and research institutions, to explore cutting-edge methodologies that ensure technology and operational excellence in prognostics and health management systems.

• Building and Establishing Trust in AI-Driven Maintenance Decisions Through Reliability and Explainability
• Real-World Implementation Challenges
• EU AI Act Risk Assessment and Classification
• Future of PHM using AI

Moderator: Dave Larsen (Collins)

• Thorbjörn Fransson, Saab
• Paul Harding, Aviation Requirements
• Bruce Steven, University of Strathclyde

Abstract:

Transportation and other industries are seeing a boom in the requirements for, and implementation of, fielded PHM systems.  These opportunities include expansion of traditional physics-based health monitoring systems, continued proliferation of statistical analysis solutions, and a new wave of AI-based approaches.  In the highly competitive industrial landscape, an optimized approach to PHM can be a gating factor in the market success or failure of new products.  We must learn from previous trials and achievements, ensuring that we foresee and avoid potential obstacles in new PHM approaches.

In this panel, experienced participants will discuss their history of notable successes or failures in the industrial implementation of PHM systems – data gathering, provisioning, and analysis – and point to lessons learned that can pave the way to better initial approaches going forward.

Moderator: Knut Erik Knutsen, DNV

• Frank Børre Pedersen, DNV
• Yiliu Liu, NTNU
• Pieter Hyuskens, Damen

Abstract:

The maritime sector faces increasing complexity with the rise of automation, digitalization, and sustainability demands. Prognostics and Health Management (PHM) offers a transformative approach to enhance safety by enabling predictive insights into equipment health and operational risks. Unlike traditional reactive or time-based maintenance, PHM integrates condition monitoring, fault diagnosis, and remaining useful life prediction to prevent failures before they occur. This is critical in maritime operations where system downtime or catastrophic failures can lead to severe safety, environmental, and economic consequences. While traditional safety frameworks emphasize preventing consequences of failures through design redundancy, fail-safes, and proven components, PHM focus on predicting failures during operation. The panel will explore how PHM can be applied across ship systems to mitigate hazards and support compliance with safety standards. Panelists from industry and academia will discuss implementation challenges, data-driven strategies, and the role of PHM in enabling autonomous and conventional vessels to operate safely under demanding condition

• What barriers do you see to uptake of PHM in maritime?
• How can PHM model failures including uncertainty to improve safety beyond what statistical failure-rate approaches can capture?
• Have you seen success stories where PHM or condition based maintenance has been well implemented in maritime?
• Have you seen cases that were not successful and can you provide learnings from this experience?

Moderator: Maneesh Singh, Western Norway University of Applied Sciences

• Carl-Christian Thodesen, Oslo Metropolitan University
• Capella Festa, Schlumberger Foundation
• Are Straume, Norwegian Directorate for Higher Education
• TBD, DNV

Abstract:

European research and innovation policy increasingly promotes cross-border collaboration, societal impact, and responsible innovation. Initiatives such as the European Research Area, Horizon Europe, and the Industry 5.0 agenda highlight the need for strong, long-term partnerships between universities, industry, and public actors to co-create knowledge, speed up innovation, and deliver lasting value to society.

In practice, multinational university–industry collaboration brings many forms of diversity. These include differences in culture, organisational roles, regulatory systems, funding models, time horizons, and ways of working.

While diversity is often seen as a driver of innovation, resilience, and inclusion, it can also create organisational and managerial challenges that are frequently underestimated in policy design and partnership governance.

The purpose of this panel is to bring together senior leaders from industry, universities, and public organisations to share their experiences with multinational university–industry partnerships. The discussion will focus on how such partnerships are created, managed, and sustained, and how diversity affects their success.
Rather than focusing on specific projects or technical solutions, he panel will focus on strategic, organisational, and human aspects of collaboration, drawing on real experiences from different countries and sectors.

• What role should public policy and funding play in supporting inclusive, trust-based, and long-term collaboration?
• What lessons from existing multinational partnerships should guide future policy and institutional strategies?
• How can partnerships support talent attraction, mobility, and retention in line with European objectives, while avoiding fragmentation of knowledge and effort?
• How do cultural, institutional, and organizational differences influence university–industry collaboration in multinational settings?
• What does diversity mean in practice, for example in decision-making, leadership styles, and expectations of outcomes?
• How can differences between academic and industrial priorities, such as long-term research versus short-term delivery, be managed constructively?
• What governance models, incentives, and communication practices help partnerships move from “collaboration on paper” to real impact?
• How should multinational.

The panel will be organised as a moderated strategic dialogue. Each panellist will begin with a short reflection, followed by an open discussion among the panellists and with the audience. The focus will be on leadership experience, policy relevance, and practical insights, rather than technical or project-specific details.