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arXiv · 2411.05887

Predictive Digital Twin for Condition Monitoring Using Thermal Imaging

Daniel Menges·Florian Stadtmann·Henrik Jordheim·Adil Rasheed
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Abstract

This paper explores the development and practical application of a predictive digital twin specifically designed for condition monitoring, using advanced mathematical models and thermal imaging techniques. Our work presents a comprehensive approach to integrating Proper Orthogonal Decomposition (POD), Robust Principal Component Analysis (RPCA), and Dynamic Mode Decomposition (DMD) to establish a robust predictive digital twin framework. We employ these methods in a real-time experimental setup involving a heated plate monitored through thermal imaging. This system effectively demonstrates the digital twin's capabilities in real-time predictions, condition monitoring, and anomaly detection. Additionally, we introduce the use of a human-machine interface that includes virtual reality, enhancing user interaction and system understanding. The primary contributions of our research lie in the demonstration of these advanced techniques in a tangible setup, showcasing the potential of digital twins to transform industry practices by enabling more proactive and strategic asset management.

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Daniel Menges, Florian Stadtmann, Henrik Jordheim, Adil Rasheed. 2024-11-08. Predictive Digital Twin for Condition Monitoring Using Thermal Imaging. https://arxiv.org/abs/2411.05887

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