Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.05887 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909382338412544 |
|---|---|
| author | Menges, Daniel Stadtmann, Florian Jordheim, Henrik Rasheed, Adil |
| author_facet | Menges, Daniel Stadtmann, Florian Jordheim, Henrik Rasheed, Adil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05887 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predictive Digital Twin for Condition Monitoring Using Thermal Imaging Menges, Daniel Stadtmann, Florian Jordheim, Henrik Rasheed, Adil Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Predictive Digital Twin for Condition Monitoring Using Thermal Imaging |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.05887 |