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Reliability modeling and simulation: advancements with data-driven techniques and expert knowledge integration

Lazarova-Molnar, Sanja ORCID iD icon 1; Friederich, Jonas; Niloofar, Parisa
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)

Abstract:

Reliability of a system defines the likelihood that a system would operate as expected for a predefined length of time.
There have been numerous notable efforts to model systems with the goal of analyzing their reliability and possibly quan-
tifying it. Simulation as a technique has shown a great capacity to address the challenge of quantitatively evaluating sys-
tem’s reliability. Traditionally, expert knowledge has been the gold standard for modeling and analyzing system reliability.
However, the prevalence of data that stems from systems of interest, enabled through the easily available Internet of
Things devices, coupled with simulation, presents a potential to change the way reliability of systems is being evaluated.
Namely, data provides an opportunity for both automated modeling and continuous reliability assessment of systems
through simulation. This article presents an overview of reliability modeling and simulation for cyber-physical systems,
highlighting opportunities arising from data availability and its integration with expert knowledge. We use case studies
from our research to illustrate these opportunities and advancements.

Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 0037-5497, 1741-3133
KITopen-ID: 1000178498
Erschienen in SIMULATION
Verlag SAGE Publications
Vorab online veröffentlicht am 30.12.2024
Nachgewiesen in Scopus
Web of Science
Dimensions

Verlagsausgabe §
DOI: 10.5445/IR/1000178498
Veröffentlicht am 29.01.2025
Seitenaufrufe: 10
seit 29.01.2025
Downloads: 6
seit 02.02.2025
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