An Event-Driven Fault Localization Algorithm Based on Incremental Bayesian Suspected Degree
一种基于增量贝叶斯疑似度的事件驱动故障定位算法

Most fault localization techniques is based on time windows. The size of time windows impacts on the accuracy of the algorithms greatly. This paper takes weighted bipartite graph as fault propagation model and proposes a heuristic fault localization algorithm based on Incremental Bayesian Suspected Degree (IBSD) to eliminate the above shortcomings. IBSD sequentially analyzes the incoming symptoms in an event-driven way and incrementally computes the Bayesian Suspected Degree and determine the most probable fault set for the current observed symptoms. Simulation results show that the algorithm has high fault detection ratio as well as low false positive ratio and has a good performance even in the presence of unobserved alarms. The algorithm which has a polynomial computational complexity could be applied to large scale communication network.