Predictive Digital Twin for Condition Monitoring Using Thermal Imaging

• URL: http://arxiv.org/abs/2411.05887v1
• Date: Fri, 08 Nov 2024 11:23:57 GMT
• Title: Predictive Digital Twin for Condition Monitoring Using Thermal Imaging
• Authors: Daniel Menges, Florian Stadtmann, Henrik Jordheim, Adil Rasheed,
• Abstract summary: This paper explores the development and practical application of a predictive digital twin specifically designed for condition monitoring. We employ advanced mathematical models and thermal imaging techniques to establish a robust digital twin framework. We introduce the use of a human-machine interface that includes virtual reality, enhancing user interaction and system understanding.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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