Approaching dynamic reliability with predictive and diagnostic purposes by exploiting dynamic Bayesian networks

We talk about dynamic reliability when the reliability parameters of the system, such as the failure rates, vary according to the current state of the system. In this article, several versions of a benchmark on dynamic reliability taken from the literature are examined. Each version deals with particular aspects such as state-dependent failure rates, failure on demand, and repair. In dynamic reliability evaluation, the complete behavior of the system has to be taken into account, instead of the only failure propagation as in fault tree analysis. To this aim, we exploit dynamic Bayesian networks and the software tool RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), with the goal of computing the system unreliability. Because of the coherence between the results returned by dynamic Bayesian network analysis and those obtained by means of other methods, together with the possibility to compute diagnostic indices, we propose dynamic Bayesian network and RADYBAN to be a valid approach to dynamic reliability evaluation.

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