A PROBABILISTIC APPROACH TO FAULT-DIAGNOSIS IN LINEAR LIGHTWAVE NETWORKS

The application of probabilistic reasoning to fault diagnosis in Linea r Lightwave Networks (LLN's) is investigated. The LLN inference model is represented by a Bayesian network (or causal network). An inference algorithm is proposed that is capable of conducting fault diagnosis ( inference) with incomplete evidence and on an interactive basis. Two b elief updating algorithms are presented which are used by the inferenc e algorithm for performing fault diagnosis. The first belief updating algorithm is a simplified version of the one proposed by Pearl for sin gly connected inference models. The second belief updating algorithm a pplies to multiply connected inference models and is more general than the first. We also introduce a t-fault diagnosis system and an adapti ve diagnosis system to further reduce the computational complexity of the fault diagnosis process.