Hybrid Bayesian Networks for the Reliability Analysis of Systems with Continuous Variables

Abstract

The standard way of dealing with continuous variables
into reliability models is to discretize (or even binarise)
them, resulting in discrete state models. The present
paper proposes an approach where continuous system
variables can be directly exploited by resorting to Hybrid
Bayesian Networks (HBN), where both continuous
and discrete variables can be mixed in a general way.
This allows one to: model the inter-dependencies between
discrete state components or subsystems, model
the inter-dependencies between continuous system variables,
model the influence of contextual information on
system variables and components, model the definition
of specific system events or conditions given specific
values of the system variables. We will show how the
above issues can be captured in a principled way by
the HBN formalism, by making the final analyses more
grounded on the actual values of every system variable.
We finally present a case study where the model of a
granule storage tank system of a petrochemical plant is
considered, and we present the results of specific analyses
implemented as inference on the HBN model.