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Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Pilastre, Barbara and Boussouf, Loic and Escrivan, Stéphane d' and Tourneret, Jean-Yves Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning. (2020) Signal Processing, 168. 1-10. ISSN 0165-1684

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Official URL: https://doi.org/10.1016/j.sigpro.2019.107320

Abstract

Spacecraft health monitoring and failure prevention are major issues in space operations. In recent years, machine learning techniques have received an increasing interest in many fields and have been applied to housekeeping telemetry data via semi-supervised learning. The idea is to use past telemetry describing normal spacecraft behaviour in order to learn a reference model to which can be compared most recent data in order to detect potential anomalies. This paper introduces a new machine learning method for anomaly detection in telemetry time series based on a sparse representation and dictionary learning. The main advantage of the proposed method is the possibility to handle multivariate telemetry time series described by mixed continuous and discrete parameters, taking into account the potential correlations be- tween these parameters. The proposed method is evaluated on a representative anomaly dataset obtained from real satellite telemetry with an available ground-truth and compared to state-of-the-art algorithms.

Item Type:Article
HAL Id:hal-02466360
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Airbus (FRANCE)
French research institutions > Centre National d'Études Spatiales - CNES (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Ecole Nationale de l'Aviation Civile - ENAC (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Collins Aerospace (FRANCE)
Other partners > Thales (FRANCE)
Other partners > Telecom ParisTech (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Direction Générale de l’Aviation Civile - DGAC (FRANCE)
Laboratory name:
Funders:
CNES - Airbus Defence and Space
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Deposited On:04 Feb 2020 13:12

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