![[2602.22267] Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22267] Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin
Summary
This paper discusses the development of a digital twin for thermal-hydraulic processes, focusing on real-time supervision, fault detection, and diagnosis using machine learning and numerical simulations.
Why It Matters
The integration of digital twins in industrial processes enhances monitoring and predictive maintenance, crucial for safety and efficiency. This research contributes to the growing field of data-driven process supervision, which is vital for modern manufacturing and energy systems.
Key Takeaways
- The digital twin framework aids in real-time monitoring of thermal-hydraulic processes.
- Machine learning models enhance fault detection and diagnosis capabilities.
- The proposed methods show promising accuracy in parameter change detection.
Computer Science > Machine Learning arXiv:2602.22267 (cs) [Submitted on 25 Feb 2026] Title:Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin Authors:Osimone Imhogiemhe (LS2N, LS2N - équipe SIMS, Nantes Univ - ECN), Yoann Jus (Cetim), Hubert Lejeune (Cetim), Saïd Moussaoui (LS2N, LS2N - équipe SIMS, Nantes Univ - ECN) View a PDF of the paper titled Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin, by Osimone Imhogiemhe (LS2N and 7 other authors View PDF Abstract:The real-time supervision of production processes is a common challenge across several industries. It targets process component monitoring and its predictive maintenance in order to ensure safety, uninterrupted production and maintain high efficiency level. The rise of advanced tools for the simulation of physical systems in addition to data-driven machine learning models offers the possibility to design numerical tools dedicated to efficient system monitoring. In that respect, the digital twin concept presents an adequate framework that proffers solution to these challenges. The main purpose of this paper is to develop such a digital twin dedicated to fault detection and diagnosis in the context of a thermal-hydraulic process supervision. Based on a numerical simulation of the system, in addition to machine learning methods, we propose different modules dedicated to process parameter change detection and their on-line estimation. T...
