Telemetry Data-based Spacecraft Anomaly Detection Using Generative Adversarial Networks

The telemetry data of spacecraft is an ultra-high dimensional time series used to indicate on-orbit operation status, and anomaly detection can effectively ensure safety and reliability. Aiming at the characteristics and complex correlation of high dimensional telemetry data, this paper proposes a n...

Full description

Saved in:
Bibliographic Details
Published in2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) pp. 297 - 301
Main Authors Song, Yue, Yu, Jinsong, Tang, Diyin, Han, Danyang, Wang, Sen
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.10.2020
Subjects
Anomaly detection
anomaly score
Feature extraction
Gallium nitride
Generative adversarial networks
Generators
multivariable time series
Telemetry
telemetry data
Time series analysis
Online AccessGet full text

Cover

More Information
Summary:The telemetry data of spacecraft is an ultra-high dimensional time series used to indicate on-orbit operation status, and anomaly detection can effectively ensure safety and reliability. Aiming at the characteristics and complex correlation of high dimensional telemetry data, this paper proposes a novel anomaly detection method based on Generative Adversarial Networks (GAN) for telemetry data anomaly detection. Instead of treating each variable independently, our proposed method captures the latent representation amongst multi-dimensional time series. For normal data, the GAN-based anomaly detection method can obtain a reconstructed time series similar to the original time series, learning the probability distribution model of normal data. For abnormal data, the reconstructed time series deviates greatly from the original time series. In the GAN framework, we use Long Short-Term Memory (LSTM) as the network structure of generator for time series reconstruction and discriminator for calculating the probability of being the real time series, which can learn the temporal features of telemetry data. We also propose a novel anomaly score called GDScore, which comprehensively considers the reconstruction error of the generator and the output of the discriminator. We conduct experiments with two telemetry datasets, which verifies that our proposed GAN-based anomaly detection method can effectively detect outliers.
DOI:10.1109/ICSMD50554.2020.9261736