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Advances in Self-Organizing Maps, José Principe, Risto Miikkulainen (Ed.) (2009) 37-44
Fault prediction in aircraft engines using Self-Organizing Maps
Marie Cottrell 1, 2, Patrice Gaubert 1, 2, Cédric Eloy 3, Damien François 3, Geoffroy Hallaux 3, Jérôme Lacaille 4, Michel Verleysen 3
Contrat de recherche avec la SNECMA YYE Collaboration(s)
(06/2009)

Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too. The maintenance can be improved if an efficient procedure for the prediction of failures is implemented. The primary source of information on the health of the engines comes from measurement during flights. Several variables such as the core speed, the oil pressure and quantity, the fan speed, etc. are measured, together with environmental variables such as the outside temperature, altitude, aircraft speed, etc. In this paper, we describe the design of a procedure aiming at visualizing successive data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them corresponding to the two-dimensional projection of the multi-dimensional vector of engine measurements. Analyzing the trajectories aims at visualizing any deviation from a normal behavior, making it possible to anticipate an operation failure.
1 :  Centre d'économie de la Sorbonne (CES)
CNRS : UMR8174 – Université Paris I - Panthéon Sorbonne
2 :  Statistique Appliquée et MOdélisation Stochastique (SAMOS)
Université Paris I - Panthéon Sorbonne
3 :  MachineLearning Group - DICE (DICE)
Université Catholique de Louvain
4 :  SNECMA YYE (SNECMA)
SNECMA
SAMOS-MATISSE http://samos.univ-paris1.fr
Statistiques/Applications

Statistiques/Théorie

Mathématiques/Statistiques
aircraft engine maintenance – fault detection – general linear models – self-organizing maps
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paper_wsom_cottrell.pdf(219.7 KB)

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