Applications de l'intelligence artificielle à la détection et l'isolation de pannes multiples dans un réseau de télécommunications

Serge Romaric Tembo Mouafo 1, 2
2 ADOPNET - Advanced technologies for operated networks
UR1 - Université de Rennes 1, IMT Atlantique - IMT Atlantique Bretagne-Pays de la Loire, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
Abstract : Telecommunication networks must be reliable and robust to ensure high availability of services. Operators are currently searching to automate as much as possible, complex network management operations such as fault diagnosis.In this thesis we are focused on self-diagnosis of failures in the optical access networks of the operator Orange. The diagnostic tool used up to now, called DELC, is an expert system based on decision rules. This system is efficient but difficult to maintain due in particular to the very large volume of information to analyze. It is also impossible to have a rule for each possible fault configuration, so that some faults are currently not diagnosed.We proposed in this thesis a new approach. In our approach, the diagnosis of the root causes of malfunctions and alarms is based on a Bayesian network probabilistic model of dependency relationships between the different alarms, counters, intermediate faults and root causes at the level of the various network component. This probabilistic model has been designed in a modular way, so as to be able to evolve in case of modification of the physical architecture of the network. Self-diagnosis of the root causes of malfunctions and alarms is made by inference in the Bayesian network model of the state of the nodes not observed in view of observations (counters, alarms, etc.) collected on the operator's network. The structure of the Bayesian network, as well as the order of magnitude of the probabilistic parameters of this model, were determined by integrating in the model the expert knowledge of the diagnostic experts on this segment of the network. The analysis of thousands of cases of fault diagnosis allowed to fine-tune the probabilistic parameters of the model thanks to an Expectation Maximization algorithm. The performance of the developed probabilistic tool, named PANDA, was evaluated over two months of fault diagnosis in Orange's GPON-FTTH network in July-August 2015. In most cases, the new system, PANDA, and the system in production, DELC, make an identical diagnosis. However, a number of cases are not diagnosed by DELC but are correctly diagnosed by PANDA. The cases for which self-diagnosis results of the two systems are different were evaluated manually, which made it possible to demonstrate in each of these cases the relevance of the decisions taken by PANDA.
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Serge Romaric Tembo Mouafo. Applications de l'intelligence artificielle à la détection et l'isolation de pannes multiples dans un réseau de télécommunications. Réseaux et télécommunications [cs.NI]. Ecole nationale supérieure Mines-Télécom Atlantique, 2017. Français. ⟨NNT : 2017IMTA0004⟩. ⟨tel-01781480⟩



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