Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-1993

Abstract

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 (1988) for singly connected inference models. The second belief updating algorithm applies to multiply connected inference models and is more general than the first. The authors also introduce a t-fault diagnosis system and an adaptive diagnosis system to further reduce the computational complexity of the fault diagnosis process

Discipline

Information Security

Research Areas

Information Security and Trust

Publication

IEEE Journal of Selected Areas in Communications

Volume

11

Issue

9

First Page

1438

Last Page

1448

ISSN

0733-8716

Identifier

10.1109/49.257935

Publisher

IEEE

Additional URL

https://doi.org/10.1109/49.257935

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