用热成像与数学模型构建可实时预测的数字孪生系统
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging
- 融合POD、RPCA与DMD三种方法建模热成像数据
- 在加热板实验中实现毫秒级实时状态预测与异常检测
- 配套虚拟现实界面，适合工业运维与设备监控场景
本文探讨了基于热成像技术的预测型数字孪生系统的开发与实际应用。研究提出一种综合框架，整合了本征正交分解（POD）、鲁棒主成分分析（RPCA）与动态模态分解（DMD），用于构建稳健的预测型数字孪生系统。实验在实时加热板监测场景中进行，通过热成像获取数据，验证了该系统在实时预测、状态监控与异常检测方面的有效性。此外，引入包含虚拟现实的人机交互界面，提升用户对系统的理解与操作体验。主要贡献在于将上述先进算法应用于真实场景，展示了数字孪生在推动资产主动管理与工业智能化转型中的潜力。
原文摘要 · Abstract (English)
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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