A Probabilistic Approach to Fault Diagnosis in Linear Lightwave Networks

The application of probabilistic reasoning to fault diagnosis in linear lightwave networks (LLNs) 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 belief updating algorithms are presented which are used by the inference algorithm for performing fault diagnosis. The first belief updating algorithm is a simplified version of the one proposed by Pearl for singly connected inference models. The second belief updating algorithm applies to multiply connected inference models and is more general than the first. We also introduce a t-fault diagnosis system and an adaptive diagnosis system to further reduce the computational complexity of the fault diagnosis process.