Representations for anomaly detection: Application to aircraft engine vibration data.

Summary Vibration measurements are one of the most relevant data to detect engine anomalies. Vibrations are acquired on a test bench during acceleration and deceleration to ensure engine reliability at the end of the production line. These temporal data are converted into spectrograms to allow the experts to perform a visual analysis of these data and to detect the various atypical signatures. The vibratory sources correspond to lines on the spectrograms. In this thesis, we have implemented an automatic decision support tool to analyze the spectrograms and detect any type of atypical signatures, these signatures do not necessarily come from an engine damage. First, we built a digital database of annotated spectrograms. It is important to note that unusual signatures are variable in shape, intensity and position and are found in a small amount of data. Therefore, to detect these signatures, we characterize the normal behaviors of the spectrograms, analogous to novelty detection methods, by representing the patches of the spectrograms on dictionaries such as curvelets and Non-negative matrix factorization (NMF), as well as by estimating the distribution of each point of the spectrogram from normal data depending or not on their neighborhood. The detection of atypical points is performed by comparing the test data to the normality model estimated on normal training data. The detection of atypical points allows the detection of unusual signatures composed by these points.