# Predictive Digital Twin for Condition Monitoring Using Thermal Imaging

arXiv: https://arxiv.org/abs/2411.05887 · HTML: https://mlsanity.com/paper/2411.05887/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Digital Twin: Values, Challenges and Enablers](https://mlsanity.com/paper/1910.01719/) (2019)
- [Data-Driven Sparse Sensor Placement for Reconstruction](https://mlsanity.com/paper/1701.07569/) (2017)
- [Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution](https://mlsanity.com/paper/2103.14629/) (2021)
- [Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions](https://mlsanity.com/paper/2304.11405/) (2023)
- [Standalone, Descriptive, and Predictive Digital Twin of an Onshore Wind Farm in Complex Terrain](https://mlsanity.com/paper/2307.02097/) (2023)
- [Data Integration Framework for Virtual Reality Enabled Digital Twins](https://mlsanity.com/paper/2401.02193/) (2024)
- [Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data](https://mlsanity.com/paper/2403.19721/) (2024)

## Built on by (most cited first, 2 of 2)

- [Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2511.02957/) (2025)
- [Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room](https://mlsanity.com/paper/2602.08949/) (2026)

## Abstract (arXiv)

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.