Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks

Résumé

This paper presents insights on the promises of probabilistic modeling and machine learning for fault diagnosis in optical access networks. A Bayesian inference engine, called Probabilistic tool for GPON-FTTH Access Network self-DiAgnosis (PANDA), is applied to fault diagnosis of Gigabit capable Passive Optical Networks (GPON). PANDA approach has been assessed on real diagnosis data, showing very satisfactory alignment with an operational rule-based expert system. Furthermore, it provides diagnosis conclusions for all tested cases, even if some monitoring data is missing or incomplete. Finally, an expectation maximization algorithm allows to finely tune the probabilistic model.
Fichier principal
Vignette du fichier
ONDM2017 Machine Learning for FTTH Diagnosis camera-ready 2017_04_14.pdf (151.47 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01573963 , version 1 (11-08-2017)

Identifiants

Citer

Stephane Gosselin, Jean-Luc Courant, Serge Romaric Tembo Mouafo, Sandrine Vaton. Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks. ONDM 2017 : 21st Conference on Optical Network Design and Modeling, May 2017, Budapest, Hungary. pp.1 - 3, ⟨10.23919/ONDM.2017.7958529⟩. ⟨hal-01573963⟩
742 Consultations
453 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More