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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01550 |
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| _version_ | 1866913922261450752 |
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| author | Kohl, Johannes Muck, Georg Jäger, Georg Zug, Sebastian |
| author_facet | Kohl, Johannes Muck, Georg Jäger, Georg Zug, Sebastian |
| contents | With the rapid development of more complex robots, Fault Detection and Diagnosis (FDD) becomes increasingly harder. Especially the need for predetermined models and historic data is problematic because they do not encompass the dynamic and fast-changing nature of such systems. To this end, we propose a concept that actively generates a dynamic system model at runtime and utilizes it to locate root causes. The goal is to be applicable to all kinds of robotic systems that share a similar software design. Additionally, it should exhibit minimal overhead and enhance independence from expert attention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic System Model Generation for Online Fault Detection and Diagnosis of Robotic Systems Kohl, Johannes Muck, Georg Jäger, Georg Zug, Sebastian Robotics With the rapid development of more complex robots, Fault Detection and Diagnosis (FDD) becomes increasingly harder. Especially the need for predetermined models and historic data is problematic because they do not encompass the dynamic and fast-changing nature of such systems. To this end, we propose a concept that actively generates a dynamic system model at runtime and utilizes it to locate root causes. The goal is to be applicable to all kinds of robotic systems that share a similar software design. Additionally, it should exhibit minimal overhead and enhance independence from expert attention. |
| title | Dynamic System Model Generation for Online Fault Detection and Diagnosis of Robotic Systems |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.01550 |