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Statistiques en grande dimension pour la détection d'anomalies dans les données fonctionnelles issues des satellites

Abstract : In this PhD, we have developed statistical methods to detect abnormal events in all the functional data produced by the satellite all through its lifecycle. The data we are dealing with come from two main phases in the satellite’s life, telemetries and test data. A first work on this thesis was to understand how to highlight the outliers thanks to projections onto functional bases. On these projections, we have also applied several outlier detection methods, such as the One-Class SVM, the Local Outlier Factor (LOF). In addition to these two methods, we have developed our own outlier detection method, by taking into account the seasonality of the data we consider. Based on this study, we have developed an original procedure to select automatically the most interesting coefficients in a semi-supervised framework for the outlier detection, from a given projection. Our method is a multiple testing procedure where we apply the two sample-test to all the levels of coefficients.We have also chosen to analyze the covariance matrices representing the covariance of the te- lemetries between themselves for the outlier detection in multivariate data. In this purpose, we are comparing the covariance of a cluster of several telemetries deriving from two consecutive days, or consecutive orbit periods. We have applied three statistical tests targeting this same issue with different approaches. We have also developed an original asymptotic test, inspired by both first tests. In addition to the proof of the convergence of this test, we demonstrate thanks to examples that this new test is the most powerful. In this PhD, we have tackled several aspects of the anomaly detection in the functional data deriving from satellites. For each of these methods, we have detected all the major anomalies, improving significantly the false discovery rate.
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Submitted on : Monday, October 1, 2018 - 5:24:07 PM
Last modification on : Thursday, March 5, 2020 - 5:57:26 PM
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  • HAL Id : tel-01885331, version 1


Clementine Barreyre. Statistiques en grande dimension pour la détection d'anomalies dans les données fonctionnelles issues des satellites. Statistiques [math.ST]. INSA de Toulouse, 2018. Français. ⟨NNT : 2018ISAT0009⟩. ⟨tel-01885331⟩



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