用于状态监测的预测性数字孪生：基于热成像技术
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging
AI总结：
本文提出整合POD、RPCA和DMD构建预测性数字孪生框架，结合热成像与虚拟现实界面，在加热板实验中验证了其在状态监测、异常检测和实时预测方面的能力，推动工业资产管理变革。
摘要：
本文探讨了一种专为状态监测设计的预测性数字孪生的开发与实际应用，结合了先进的数学模型与热成像技术。我们提出了一种综合方法，通过整合本征正交分解（POD）、鲁棒主成分分析（RPCA）和动态模态分解（DMD），构建了一个稳健的预测性数字孪生框架。我们在一个实时实验环境中应用了这些方法，该环境包含一块通过热成像监测的加热板。该系统有效展示了数字孪生在实时预测、状态监测和异常检测方面的能力。此外，我们引入了包含虚拟现实的人机界面，增强了用户交互与系统理解。本研究的主要贡献在于通过具体装置验证了这些先进技术，展示了数字孪生通过实现更具前瞻性和战略性的资产管理来变革工业实践的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
