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

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.