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Erschienen in:
PySysML2: Building Knowledge from Models with SysML v2 and Python Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Model-Based Verification Strategies Using SysML and Bayesian Networks

verfasst von : Joe Gregory, Alejandro Salado

Erschienen in: The Proceedings of the 2023 Conference on Systems Engineering Research

Verlag: Springer Nature Switzerland

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Abstract

In this chapter, the authors outline an approach to formally model verification strategies using Systems Modeling Language (SysML) in a way that enables the automatic generation of the corresponding Bayesian network. The approach includes the development of a verification metamodel that can be represented as a SysML profile. A notional example is included, in which a CubeSat verification strategy is produced in accordance with the SysML profile and a representative Bayesian network is created. Results from the Bayesian update are presented, and the impact on the SysML model is discussed. Further work will focus on the continued development of this metamodel, the integration of the plug-in to automatically generate the corresponding Bayesian network, and more detailed case studies.

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Vorheriges Kapitel PySysML2: Building Knowledge from Models with SysML v2 and Python
Nächstes Kapitel MBSE-Based Design Space Exploration for Productivity Improvement Using Workflow Models
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Metadaten
Titel
Model-Based Verification Strategies Using SysML and Bayesian Networks
verfasst von
Joe Gregory
Alejandro Salado
Copyright-Jahr
2024
Buch
The Proceedings of the 2023 Conference on Systems Engineering Research

Print ISBN: 978-3-031-49178-8

Electronic ISBN: 978-3-031-49179-5

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-49179-5_2

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