Modeling cascading failure propagation through dynamic Bayesian Networks

Capturing dependencies in dependability studies is one of the most challenging tasks and requires suitable modeling techniques. In recent years the growing interest toward complex critical infrastructures and their interdependencies has solicited new efforts in the area of modeling and analysis of large interdependent systems. Cascading failures are a typical phenomenon of dependencies of components inside a system or among systems. New research in the area of the analysis of cascading phenomena has been also dictated by the recent occurrence of large scale electrical blackouts both in USA and in Europe that have caused the shortage of electrical power to millions of citizens. The present paper proposes to model cascading failures by means of Dynamic Bayesian Networks (DBN). In contrast with available techniques, DBN offer a good trade-off between the analytical tractability and the representation of the propagation of the cascading event. A failure scenario, taken from the literature, is considered as a final example.