L’Ecole doctorale : Sciences et Technologies de l’Information et de la Communication et le Laboratoire de recherche SAMOVAR pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur Nicolas AUSSEL<\/p>\n
Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Universit\u00e9 Paris-Saclay, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en :
\nMath\u00e9matiques et Informatique
\n\u00ab D\u00e9tection des anomalies sur les donn\u00e9es en vol en temps r\u00e9el avec des agents communicants h\u00e9t\u00e9rog\u00e8nes \u00bb<\/p>\n
le VENDREDI 21 JUIN 2019 \u00e0 10h00<\/p>\n Salle A003<\/p>\n T\u00e9l\u00e9com SudParis – 9 Rue Charles Fourier 91011 Evry<\/strong><\/p>\n Membres du jury :<\/strong><\/p>\n R\u00e9sum\u00e9 :<\/strong><\/p>\n Avec l’augmentation du nombre de capteurs et d’actuateurs dans les avions et le d\u00e9veloppement de liaisons de donn\u00e9es fiables entre les avions et le sol, il est devenu possible d’am\u00e9liorer la s\u00e9curit\u00e9 et la fiabilit\u00e9 des syst\u00e8mes \u00e0 bord en appliquant des techniques d’analyse en temps r\u00e9el. Cependant, \u00e9tant donn\u00e9 la disponibilit\u00e9 limit\u00e9 des ressources de calcul embarqu\u00e9es et le co\u00fbt \u00e9lev\u00e9 des liaisons de donn\u00e9es, les solutions architecturelles actuelles ne peuvent pas exploiter pleinement toutes les ressources disponibles, limitant leur pr\u00e9cision. Notre but est de proposer un algorithme distribu\u00e9 de pr\u00e9diction de panne qui pourrait \u00eatre ex\u00e9cut\u00e9 \u00e0 la fois \u00e0 bord de l’avion et dans une station au sol tout en respectant un budget de communication. Dans cette approche, la station au sol disposerait de ressources de calcul rapides et de donn\u00e9es historiques et l’avion disposerait de ressources de calcul limit\u00e9es et des donn\u00e9es de vol actuelles. Dans cette th\u00e8se, nous \u00e9tudierons les sp\u00e9cificit\u00e9s des donn\u00e9es a\u00e9ronautiques et les m\u00e9thodes d\u00e9j\u00e0 existantes pour produire des pr\u00e9dictions de pannes \u00e0 partir de ces derni\u00e8res et nous proposerons une solution au probl\u00e8me pos\u00e9. Notre contribution sera d\u00e9taill\u00e9 en trois parties. Premi\u00e8rement, nous \u00e9tudierons le probl\u00e8me de pr\u00e9diction d’\u00e9v\u00e9nements rares cr\u00e9\u00e9 par la haute fiabilit\u00e9 des syst\u00e8mes a\u00e9ronautiques. Beaucoup de m\u00e9thodes d’apprentissage en classification reposent sur des jeux de donn\u00e9es \u00e9quilibr\u00e9s. Plusieurs approches existent pour corriger le d\u00e9s\u00e9quilibre d’un jeu de donn\u00e9e et nous \u00e9tudierons leur efficacit\u00e9 sur des jeux de donn\u00e9es extr\u00eamement d\u00e9s\u00e9quilibr\u00e9s. Deuxi\u00e8mement, nous \u00e9tudierons le probl\u00e8me d’analyse textuelle de journaux car de nombreux syst\u00e8mes a\u00e9ronautiques ne produisent pas d’\u00e9tiquettes ou de valeurs num\u00e9riques faciles \u00e0 interpr\u00e9ter mais des messages de journaux textuels. Nous \u00e9tudierons les m\u00e9thodes existantes bas\u00e9es sur une approche statistique et sur l’apprentissage profond pour convertir des messages de journaux textuels en une forme utilisable en entr\u00e9e d’algorithmes d’apprentissage pour classification. Nous proposerons notre propre m\u00e9thode bas\u00e9e sur le traitement du langage naturel et montrerons comment ses performances d\u00e9passent celles des autres m\u00e9thodes sur un jeu de donn\u00e9e public standard. Enfin, nous offrirons une solution au probl\u00e8me pos\u00e9 en proposant un nouvel algorithme d’apprentissage distribu\u00e9 s’appuyant sur deux paradigmes d’apprentissage existant, l’apprentissage actif et l’apprentissage f\u00e9d\u00e9r\u00e9. Nous d\u00e9taillerons notre algorithme, son impl\u00e9mentation et fournirons une comparaison de ses performances avec les m\u00e9thodes existantes.<\/p>\n Abstract :<\/strong><\/p>\n With the rise of the number of sensors and actuators in an aircraft and the development of reliable data links from the aircraft to the ground, it becomes possible to improve aircraft security and maintainability by applying real-time analysis techniques. However, given the limited availability of on-board computing and the high cost of the data links, current architectural solutions cannot fully leverage all the available resources limiting their accuracy. Our goal is to provide a distributed algorithm for failure prediction that could be executed both on-board of the aircraft and on a ground station and that would produce on-board failure predictions in near real-time under a communication budget. In this approach, the ground station would hold fast computation resources and historical data and the aircraft would hold limited computational resources and current flight’s data. In this thesis, we will study the specificities of aeronautical data and what methods already exist to produce failure prediction from them and propose a solution to the problem stated. Our contribution will be detailed in three main parts. First, we will study the problem of rare event prediction created by the high reliability of aeronautical systems. Many learning methods for classifiers rely on balanced datasets. Several approaches exist to correct a dataset imbalance and we will study their efficiency on extremely imbalanced datasets. Second, we study the problem of log parsing as many aeronautical systems do not produce easy to classify labels or numerical values but log messages in full text. We will study existing methods based on a statistical approach and on Deep Learning to convert full text log messages into a form usable as an input by learning algorithms for classifiers. We will then propose our own method based on Natural Language Processing and show how it outperforms the other approaches on a public benchmark. Last, we offer a solution to the stated problem by proposing a new distributed learning algorithm that relies on two existing learning paradigms Active Learning and Federated Learning. We detail our algorithm, its implementation and provide a comparison of its performance with existing methods<\/p>\n","protected":false},"excerpt":{"rendered":" L’Ecole doctorale : Sciences et Technologies de l’Information et de la Communication et le Laboratoire de recherche SAMOVAR pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur Nicolas AUSSEL Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Universit\u00e9 Paris-Saclay, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : Math\u00e9matiques et Informatique \u00ab D\u00e9tection des anomalies sur les 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\n Mme Sophie CHABRIDON,
\nDirectrice d’\u00e9tudes, T\u00e9l\u00e9com SudParis, FRANCE <\/td>\nDirectrice de th\u00e8se<\/td>\n<\/tr>\n \n M. Yohan PETETIN,
\nMa\u00eetre de conf\u00e9rences, T\u00e9l\u00e9com SudParis, FRANCE<\/td>\nEncadrant<\/td>\n<\/tr>\n \n M. Pierre SENS,
\nProfesseur, Universit\u00e9 Paris 6, FRANCE<\/td>\nRapporteur<\/td>\n<\/tr>\n \n M. Mustapha LEBBAH,
\nAssociate Professor, Universit\u00e9 Paris 13, FRANCE<\/td>\nRapporteur<\/td>\n<\/tr>\n \n M. Eric GRESSIER-SOUDAN,
\nProfesseur, CNAM, FRANCE <\/td>\nExaminateur<\/td>\n<\/tr>\n \n Mme Mathilde MOUGEOT,
\nProfesseur, ENSIIE, FRANCE<\/td>\nExaminatrice<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n