The paper presents a general approach to assess the performance of a networked system, made up of one or more unfaultable “source” nodes, unfaultable “user” nodes and directed edges subjected to failure and repair; a weight is assigned to each user, basing on the amount of disutility produced when it is not connected to a source [2]. The Global performance of the network is defined as the weighted sum of the probability that each user is not connected to a source (Local performances) [10]. An algorithm based on Cellular Automata is applied in order to evaluate the state of all “end-user” nodes for each system configuration [8]. MonteCarlo techniques are applied to simulate the “random walk” among the system configurations, following an “indirect” approach ([3], [4]). The use of importance measures to rank scenario and network’s elements is proposed by several authors [4]; our approach concerns their evaluation basing on a metric referred to the whole system. The relationships among the main importance measures are provided graphically by the so-called “risk impact curves” ([5], [9]). The presented approach is applied to a simple network in order to investigate its structure, verifying the obtained results through the “enumeration of the system state” [10].
Reliability assessment basing on importance measures
LA ROVERE, STEFANO;VESTRUCCI, PAOLO;SPERANDII, MARIA
2008
Abstract
The paper presents a general approach to assess the performance of a networked system, made up of one or more unfaultable “source” nodes, unfaultable “user” nodes and directed edges subjected to failure and repair; a weight is assigned to each user, basing on the amount of disutility produced when it is not connected to a source [2]. The Global performance of the network is defined as the weighted sum of the probability that each user is not connected to a source (Local performances) [10]. An algorithm based on Cellular Automata is applied in order to evaluate the state of all “end-user” nodes for each system configuration [8]. MonteCarlo techniques are applied to simulate the “random walk” among the system configurations, following an “indirect” approach ([3], [4]). The use of importance measures to rank scenario and network’s elements is proposed by several authors [4]; our approach concerns their evaluation basing on a metric referred to the whole system. The relationships among the main importance measures are provided graphically by the so-called “risk impact curves” ([5], [9]). The presented approach is applied to a simple network in order to investigate its structure, verifying the obtained results through the “enumeration of the system state” [10].I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
