E. Castillo Ron, Z. Grande Andrade
The paper explains in detail how a Bayesian network is built to perform a probabilistic safety analysis (PSA) of railway lines. The variables are the elements that the train and driver encounter when circulating along the line. Since human errors are very relevant for safety evaluation, the driver attention and its time evolution are modelled too. The nodes of the Bayesian network, their links and the associated probability tables are automatically constructed based on the line data that need to be carefully given. The conditional probability tables are reproduced by closed formulas, which facilitate the modelling and the sensitivity analysis. The most dangerous elements in the line are identified. This permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs substantially. The proposed methodology is illustrated by its application to several cases that include real lines.
Palabras clave: Bayesian networks, inference propagation, network partitionProgramado
L05.5 Sesión de la Real Sociedad de Matemática Española
5 de septiembre de 2016 11:30