
handle: 11570/3101556
Reliability block diagrams (RBD), and fault trees (FT) are the most widely used formalisms in system reliability modeling. They implement two different approaches: in a reliability block diagram, the system is represented by components connected according to their function or reliability relationships, while fault trees show which combinations of the components failures will result in a system failure. Although RBD and FT are commonly used, they are limited in their modeling capacity of systems that have no sequential relationships among their component failures. They do not provide any elements or capabilities to model reliability interactions among components or subsystems, or to represent system reliability configuration changing (dynamics), such as: load-sharing, standby redundancy, interferences, dependencies, common cause failures, and so on. To overcome this lack, Dugan et al. developed the dynamic FT (DFT). DFT extend static FT to enable modeling of time dependent failures by introducing new dynamic gates and elements. Following this way, recently we have extended the RBD into the dynamic RBD notation. Many similarities link the DFT and the DRBD formalisms, but, at the same time, one of the aims of DRBD is to extend the DFT capabilities in dynamic behavior modeling. In the paper the comparison between DFT and DRBD is studied in depth, defining a mapping of DFT elements into the DRBD domain, and investigating if and when is possible to invert the translations from DRBD to DFT. These mapping rules are applied to an example drawn from literature to show their effectiveness
Dynamic fault tree; Dynamic reliability block diagrams; Dynamic systems; System reliability; Mathematics (all); Safety, Risk, Reliability and Quality; Computer Science Applications1707 Computer Vision and Pattern Recognition
Dynamic fault tree; Dynamic reliability block diagrams; Dynamic systems; System reliability; Mathematics (all); Safety, Risk, Reliability and Quality; Computer Science Applications1707 Computer Vision and Pattern Recognition
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