Optimization of fault diagnosis based on the combination of Bayesian Networks and Case Based Reasoning

Fault diagnosis is one of the most important tasks in fault management, in fact, one of the main steps of the fault management system is to detect failures as soon as they happen in order to minimize their effects on the network and service quality perceived by users. In this paper we introduce a new hybrid approach based on Bayesian Network and Case Based Reasoning. The purpose of our approach is to address the limits of the process of fault diagnosis and propose a technique that would reduce human intervention in this process, identify the root cause with higher precision and best reliability, and particularly making the process more rapidly, by taking into consideration the dynamic topology and the identification of the root cause with great accuracy..