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Aircraft engine manufacturers collect large amount of engine related dataduring flights. These data are used to detect anomalies in the engines in orderto help companies optimize their maintenance costs. This article introduces andstudies a generic methodology that allows one to build automatic early signs ofanomaly detection in a way that is understandable by human operators who makethe final maintenance decision. The main idea of the method is to generate avery large number of binary indicators based on parametric anomaly scoresdesigned by experts, complemented by simple aggregations of those scores. Thebest indicators are selected via a classical forward scheme, leading to a muchreduced number of indicators that are tuned to a data set. We illustrate theinterest of the method on simulated data which contain realistic early signs ofanomalies.

A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation