Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin

2026-02-25

AI Summary

This work proposes a physics-informed digital twin framework that integrates physical models with machine learning to enable real-time fault diagnosis and anomaly detection in thermal-hydraulic systems. By deeply coupling numerical simulation with data-driven techniques, the framework supports online parameter estimation, fault localization, and dynamic model updating. In a test scenario involving an abrupt single-parameter change, the system accurately identified the fault location and simultaneously corrected the parameter value, demonstrating the effectiveness and practicality of the proposed approach. This study establishes a novel paradigm for intelligent monitoring of complex industrial processes, enhancing both safety and operational efficiency through synergistic use of domain knowledge and adaptive learning.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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. The proposed fault detection and diagnosis algorithm is validated on a specific test scenario, with single one-off parameter change occurrences in the system. The numerical results show good accuracy in terms of parameter variation localization and the update of their values.

Problem

• thermal-hydraulic process
• fault detection and diagnosis
• real-time supervision
• digital twin
• process monitoring

Innovation

• Digital Twin
• Thermal-Hydraulic Process
• Fault Detection and Diagnosis
• Data-Driven Supervision
• Physics-Based Simulation