@inproceedings{baglietto_bayesian_2018,
	title = {A {Bayesian} {Network} approach for the reliability analysis of complex railway systems},
	doi = {10.1109/ICIRT.2018.8641655},
	abstract = {Railway system is a typical large-scale complex system with interconnected sub-systems, each containing several components. In this framework, cost-effective asset management and innovative smart maintenance strategies require an accurate estimation of the reliability at different levels, according to the system configuration. Moreover, in order to apply risk-based maintenance approaches, techniques for the evaluation of assets criticality, that take into account the causal-effect relation between system components, are necessary. This paper presents a Bayesian Network modeling approach for the reliability evaluation of a complex rail system, which is applied to a real world case study consisting of a railway signaling system, with the aim of showing the usefulness of the approach in achieving a good understanding of the behavior of such a complex system.},
	booktitle = {2018 {International} {Conference} on {Intelligent} {Rail} {Transportation} ({ICIRT})},
	author = {Baglietto, Emanuela and Consilvio, Alice and Febbraro, Angela Di and Papa, Federico and Sacco, Nicola},
	month = dec,
	year = {2018},
	keywords = {Bayes methods, Bayesian Network, Communication system signaling, Complex systems, Maintenance engineering, Rail transportation, Rails, Railway systems, Reliability, asset criticality, complex system, fault analysis, railway, reliability analysis, signalling},
	pages = {1--6},
}