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Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article , Conference object 01 Jan 2022 Netherlands English Publisher:Springer International PublishingFunded by:EC | CAESAR
Authors: Lisandro Arturo Jimenez-Roa; Matthias Volk 0001; Mariëlle Stoelinga;

doi: 10.1007/978-3-031-14835-4_4 , 10.5281/zenodo.6539042 , 10.5281/zenodo.5571811 , 10.5281/zenodo.5571812

handle: 2066/253528

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Abstract

This dataset contains (i) the Python source code of the SymLearn toolchain and the improved implementation of the FT-MOEA algorithm (used to infer Fault Tree models in a data-driven manner); (ii) the failure dataset for five case studies. The latter used as input to our implementation; and (iii) the results obtained from our toolchain for the case studies.

This research has been partially funded by Dutch Research Council (NWO) under the grant PrimaVera (https://primavera-project.com) number NWA.1160.18.238.

Country
Netherlands
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Keywords

22/3 OA procedure, Fault tree analysis, model learning, symmetric variables, complex engineering systems, Software Science

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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