Modelling system reliability aspects of ERTMS/ETCS by Fault Trees and Bayesian Networks

Critical control systems require proper techniques to predict their failure rate since early design stages, in order to fulfil dependability requirements and minimize development costs. Bayesian Networks have been shown to be suitable to model system reliability aspects, extending the modeling power of Fault Trees and featuring a better solving efficiency with respect to Petri Nets. In this paper we exploit the Fault Tree and Bayesian Network formalisms in order to perform a hardware reliability analysis of a complex real world case study: the European Railway Traffic Management System / European Train Control System (ERTMS/ETCS). ERTMS/ETCS is a recent standard specification aimed at improving interoperability, performances and dependability of modern railways. An implementation of ERTMS/ETCS is a distributed heterogeneous system with strict reliability requirements. Starting from such requirements and from a reference hardware architecture, we studied system reliability by instantiating models with realistic parameters and performing a series of sensitivity analyses in order to highlight design trade-offs. By evaluating and integrating sub-models using a compositional approach we both obtained several interesting results and showed the effectiveness of a combined use of Fault Trees and Bayesian Networks in dealing with system reliability analyses of train con-trol systems.