Multivariate Anomaly Detection in Discrete and Continuous Telemetry Signals Using a Sparse Decomposition into a Dictionary

Abstract

This paper presents some research works based on the PhD thesis of B. Pilastre (B. Pilastre, Estimation Parcimonieuse et Apprentissage de Dictionnaires pour la détection d’Anomalies Multivariées dans des Données Mixtes de Télémesure Satellite, PhD Thesis of the university of Toulouse, Nov. 6, 2020.), supported by CNES and Airbus Defence & Space, on a new Anomaly Detection algorithm based on a sparse decomposition into a DICTionary (ADDICT). The proposed method addresses two main challenges related to anomaly detection for satellite telemetry parameters, namely the multivariate processing of these parameters and the mixed continuous and discrete nature of the data. Different variations of the ADDICT algorithm, referred to as C-ADDICT and W-ADDICT, have been investigated differing by the data decomposition term defined using a linear combination of the atoms or its convolutional equivalent. The resulting ADDICT, C-ADDICT and W-ADDICT algorithms have been evaluated on a small representative dataset containing satellite anomalies with an available ground-truth and have shown competitive results with respect to the state-of-the-art. They have also been tested on industrial use-cases, especially regarding online processing (i.e., sequential learning taking into account the feedback of users). The results of these tests are presented in this paper.