Quantitative Analysis of the Dynamic Relevance of Systems

In systems with imperfect fault coverage (IFC), all components are subject to uncovered failures, possibly threatening the whole system. Therefore, to improve the system reliability, it is important to timely detect, identify, and shut down the components that are no more relevant for the system operation. This article addresses quantitative evaluation of the relevance of components, assuming that they have independent and identically distributed lifetimes to characterize the impact of the system design only on the system reliability and energy consumption. To this end, the dynamic relevance measure is defined to characterize the irrelevant components in different stages of the system lifetime depending on the number of occurred component failures, supporting the evaluation of the probability that the system fails due to uncovered failures of irrelevant components. Moreover, the system reliability over time is also efficiently derived, both in the case that irrelevance is not considered and in the case that irrelevant components can be immediately isolated, notably supporting any general (i.e., non-Markovian) distribution for the failure time of components. Feasibility and effectiveness of the approach are assessed on two real-scale case studies addressing reliability evaluation of a flight control system and a multihop wireless sensor network.