Anomaly detection on spectrograms using data- driven and fixed dictionary representations
نویسندگان
چکیده
Spectrograms provide a visual representation of the vibrations of civil aircraft engines. The vibrations contain information relative to damage in the engine, if any. This representation is noisy, high dimensional and the relevant signatures relative to damages concern only a small part of the spectrogram. All these arguments lead to difficulties to automatically detect anomalies in the spectrogram. Adequate lower dimensional representations of the spectrograms are needed. In this paper, we study two types of representations with dictionary, a data-driven one and a non-adaptive one and we show their benefits for automatic anomaly detection.
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