Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
Dictionary Comparison for Anomaly Detection on Aircraft Engine Spectrograms
verfasst von : Mina Abdel-Sayed, Daniel Duclos, Gilles Faÿ, Jérôme Lacaille, Mathilde Mougeot
Erschienen in: Machine Learning and Data Mining in Pattern Recognition
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
To ensure the liability of civil aircrafts, engines have to be tested after their production. Vibrations are one of the most informative measures to diagnose some damages in the engine if any. The representation of these vibrations as spectrograms provides visual signatures related to damages. However, this representation is noisy and high-dimensional. Moreover, the relevant signatures are localized in small parts of the spectrogram and the number of damaged engines in the database is extremely low. These elements disturb the elaboration of detection algorithms. A new adequate representation computed from the spectrograms is needed in order to perform automatic diagnose of the aircraft engines. In this paper, we study two kinds of representations with dictionaries that can be learnt from the data (NMF) or fixed in advance (curvelets). We present some dictionary comparison methods taking into account the low number of damaged engines.