Root-Cause Diagnosis for Rare Failures Using Bayesian Network with Dynamic Modification

Abstract

We propose a root-cause diagnosis method for finding equipment suffering from rare failures in a communication network. Although many studies have been conducted on root cause diagnosis for finding failed equipment using a Bayesian Network or other methods, there has not been sufficient research into finding rare-failure equipment. Current methods are mainly focused on typical-failure equipment and cannot find rare-failure equipment. This is because rare failures have two features;unexpected causal relations and observation errors. To adapt rare- failure features, we propose a method that consists of an extended causal model and an extended inference algorithm with dynamic modification of the causal relations and observation statuses in a Bayesian Network. We experimentally evaluated its effectiveness.