Applying BN (Bayesian network) to establishing a new and effective failure inference model of equipment under uncertainties

Aim: The introduction of the full paper reviews some papers in the open literature and points out what we believe to be their shortcomings; then, it reviews some other papers on successful BN applications; finally, it proposes what we believe to be a new and effective application mentioned in the title. Section 1 explains how we established our failure inference model based on the BN; its core consists of: (1) our failure inference model uses the network topology and the probability distributions to represent the components, relationships and propagations in the equipment; (2) we divide the variables into failure detection subset, failure cause subset and failure mode subset according to their levels and causalities in the equipment; (3) we put forward the network edge orientation rule based on the maintenance engineers' actual failure reasoning processes; (4) we analyze the conditional probability distributions of the variables in the failure inference model to indicate their advantages for uncertainty representations and parameter reductions. Section 2 does the case study of a head-up display failure inference model; the results, given in Tables 4, 5 and 6, and their analysis show preliminarily that our failure inference model based on the BN is effective for equipment failure diagnosis and prediction.