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

TL;DR

This paper develops a physics-based digital twin for thermal-hydraulic process supervision, integrating numerical simulation and machine learning to detect and diagnose faults in real-time with high accuracy.

Contribution

It introduces a novel digital twin framework combining simulation and machine learning for fault detection and diagnosis in thermal-hydraulic systems.

Findings

Accurate fault detection and diagnosis demonstrated in test scenarios.

Effective online estimation of process parameter changes.

High localization accuracy of parameter variations.

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.