Représentations pour la détection d’anomalies : Application aux données vibratoires des moteurs d’avions

Abstract : Vibration measurements are one of the most relevant data for detecting anomalies in engines. Vibrations are recorded on a test bench during acceleration and deceleration phases to ensure the reliability of every flight engine at the end of the production line. These temporal signals are converted into spectrograms for experts to perform visual analysis of these data and detect any unusual signature. Vibratory signatures correspond to lines on the spectrograms. In this thesis, we have developed a decision support system to automatically analyze these spectrograms and detect any type of unusual signatures, these signatures are not necessarily originated from a damage in the engine. Firstly, we have built a numerical spectrograms database with annotated zones, it is important to note that data containing these unusual signatures are sparse and that these signatures are quite variable in shape, intensity and position. Consequently, to detect them, like in the novelty detection process, we characterize the normal behavior of the spectrograms by representing patches of the spectrograms in dictionaries such as the curvelets and the Non-negative matrix factorization (NMF) and by estimating the distribution of every points of the spectrograms with normal data depending or not of the neighborhood. The detection of the unusual points is performed by comparing test data to the model of normality estimated on learning normal data. The detection of the unusual points allows the detection of the unusual signatures composed by these points.
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Submitted on : Thursday, July 26, 2018 - 3:39:09 PM
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  • HAL Id : tel-01849893, version 1

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Mina Abdel Sayed. Représentations pour la détection d’anomalies : Application aux données vibratoires des moteurs d’avions. Mathématiques générales [math.GM]. Université Paris-Saclay, 2018. Français. ⟨NNT : 2018SACLC037⟩. ⟨tel-01849893⟩

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