Outils statistiques de traitement d'indicateurs pour le diagnostic et le pronostic des moteurs d'avions

Abstract : Identifying early signs of failures in an industrial complex system is one of the main goals of preventive maintenance. It allows to avoid failure and reduce the degradation on a component by doing an earlier maintenance operation. Health monitoring for aircraft engines is one of the industrial fields for which this anomaly detection is very important and meaningful. Aircraft engine manufacturers such as Snecma collect large amount of engine related data during each flight. The idea is to be able to automatically detect when the engine is deviating from its normal behavior. Thus Snecma is developing applications allowing people to prevent engine failures by detecting early signs of anomaly. This doctoral thesis is introdulcing how the experts' knowledge is used to process this engine related data. This first step has pointed out the difficulties in handling the data whether relating to their storage or relating to processing algorithms themselves. After that, this thesis offers a method to combine experts' knowledge with machine learning processes which follow Snecma needs such as the combination of various informations, error control or the interpretability of diagnostics results. To do that the method is focusing directly on the data from the algorithms developed by the experts themselves. This is done by homogenizing the data and then by merging these data. This step allows for the use of supervised classification algorithms whom goal are to to group the items (here the engines) of a similar nature in the same class without losing the temporal component of the information. The homogenization of the data also allows the use of monitoring applications developed by experts in order to detect anomalies. Before merging the data, a selection algorithm is used. This thesis describes how the selection process allows the monitoring algorithms to calibrate themselves. Moreover, this selection follows the first constraint imposed by Snecma concerning the interpretability of the results. Eventually, the method introduced in this thesis aims at helping Snecma make the anomalies' labels converge for all its users. It also aims at incitating to gather all the data on a single database containing : the raw and the processed data from the engine and the engine related data that could be useful such as the results from experts analysis, etc. Using this database, this thesis can then offer a labelisation tool that can be used to improve selection and classification algorithms.
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Contributor : Tsirizo Rabenoro <>
Submitted on : Friday, November 6, 2015 - 4:09:57 PM
Last modification on : Monday, November 27, 2017 - 2:14:02 PM
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  • HAL Id : tel-01225739, version 1

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Tsirizo Rabenoro. Outils statistiques de traitement d'indicateurs pour le diagnostic et le pronostic des moteurs d'avions. Machine Learning [stat.ML]. Université Paris 1 Panthéon Sorbonne, 2015. Français. ⟨tel-01225739⟩

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