Nonparametric predictive reliability of series of voting systems

Nonparametric Predictive Inference (NPI) for system reliability reflects the dependence of reliabilities of similar components due to limited knowledge from testing. NPI has recently been presented for reliability of a single voting system consisting of multiple types of components. The components are all assumed to play the same role within the system, but with regard to their reliability components of different types are assumed to be independent. The information from tests is available per type of component. This paper presents NPI for systems with subsystems in a series structure, where all subsystems are voting systems and components of the same type can be in different subsystems. As NPI uses only few modelling assumptions, system reliability is quantified by lower and upper probabilities, reflecting the limited information in the test data. The results are illustrated by examples, which also illustrate important aspects of redundancy and diversity for system reliability.

► New lower and upper predictive probabilities for system functioning.
► Systems are series of voting systems which can have exchangeable and different components.
► Nonparametric predictive inference ensures few modelling assumptions and uses component test data.
► Examples illustrate use for redundancy allocation.
► Diversity of components in system is shown to follow from limited knowledge of components’ reliability.