Dynamic System Model Generation for Online Fault Detection and Diagnosis of Robotic Systems

Fuente: arXiv
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Auteurs principaux: Kohl, Johannes, Muck, Georg, Jäger, Georg, Zug, Sebastian
Format: Preprint
Publié: 2025
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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