Downwards propagating: Bayesian analysis of complex on-demand systems

Abstract

A Bayesian approach for inference from multiple overlapping higher level data sets on component failure probabilities within complex on demand systems is presented in this paper (systemic or sub-systemic data is referred to as higher level data as it appears ‘higher’ in visualization methodologies). The approach is based on a detailed understanding of the system logic represented using fault-trees, reliability block diagrams or another similar representation. Structure functions of the relevant sensors in terms of component states are used in conjunction with the probability of all possible system states to generate the likelihood function of overlapping evidence. This forms the basis of the likelihood function used in the Bayesian analysis of the overlapping data sets.