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

Abstract

The standard way of dealing with continuous variablesinto reliability models is to discretize (or even binarise)them, resulting in discrete state models. The presentpaper proposes an approach where continuous systemvariables can be directly exploited by resorting to HybridBayesian Networks (HBN), where both continuousand discrete variables can be mixed in a general way.This allows one to: model the inter-dependencies betweendiscrete state components or subsystems, modelthe inter-dependencies between continuous system variables,model the influence of contextual information onsystem variables and components, model the definitionof specific system events or conditions given specificvalues of the system variables. We will show how theabove issues can be captured in a principled way bythe HBN formalism, by making the final analyses moregrounded on the actual values of every system variable.We finally present a case study where the model of agranule storage tank system of a petrochemical plant isconsidered, and we present the results of specific analysesimplemented as inference on the HBN model.