Name
Abstract | ||
|---|---|---|
A probabilistic event-driven fault localization technique is presented, which uses a symp- tom-fault map as a fault propagation model. The technique isolates the most probable set of faults through incremental updating of the symptom explanation hypothesis. At any time, it provides a set of alternative hypotheses, each of which is a complete explanation of the set of symptoms observed thus far. The hypotheses are ranked according to a measure of their goodness. The technique allows multiple simultaneous independent faults to be identified and incorporates both negative and positive symptoms in the analysis. As shown in a simulation study, the technique is resilient both to noise in the symptom data and to the inaccuracies of the probabilistic fault propagation model. 1 |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/INM.2003.1194216 | Integrated Network Management |
Keywords | Field | DocType |
directed graphs,fault diagnosis,probability,telecommunication network reliability,bipartite directed graph,fault propagation model,incremental updating,multiple simultaneous independent faults,noise,probabilistic event-driven fault diagnosis,probabilistic fault propagation model,simulation study,symptom explanation hypothesis,symptom-fault map | Stuck-at fault,Alternative hypothesis,Ranking,Computer science,Directed graph,Artificial intelligence,Probabilistic logic,Telecommunication network reliability,Machine learning,Fault propagation,Simple Network Management Protocol,Distributed computing | Conference |
Volume | ISSN | Citations |
118 | 1571-5736 | 15 |
PageRank | References | Authors |
1.08 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Malgorzata Steinder | 1 | 1016 | 65.74 |
| Adarshpal S. Sethi | 2 | 591 | 84.03 |