Generalising Event Trees Using Bayesian Networks with a Case Study of Train Derailment

Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between uncertain variables. Although popular in expert systems, Bayesian networks are not used widely for safety. Using a train derailment case study, we show how an event tree can be viewed as a Bayesian network, making it clearer when one event affects a later one. Since this effect needs to be understood to construct an event tree correctly, we argue that the two notations should be used together. We then show how the Bayesian Network enables the factors that influence the outcome of events to be represented explicitly. In the case study, this allowed the train derailment model to be generalised and applied in more circumstances. Although the resulting model is no longer just an event tree, the familiar event tree notation remains useful.

• Type: Conference
• Year: 2005
• Where: SAFECOMP
• Authors: George Bearfield, William Marsh
• Keywords: Bayesian Network, Event Tree, SAFECOMP 2005, Train Derailment