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Spacecraft anomaly detection with attention temporal convolution networks

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Abstract

Spacecraft faces various situations when carrying out exploration missions in complex space, thus monitoring the anomaly status of spacecraft is crucial to the development of the aerospace industry. The time-series telemetry data generated by on-orbit spacecraft contains important information about the status of spacecraft. However, traditional domain knowledge-based spacecraft anomaly detection methods are not effective due to high dimensionality and complex correlation among variables. In this work, we propose an anomaly detection framework for spacecraft multivariate time-series data based on temporal convolution networks (TCNs). First, we employ dynamic graph attention to model the complex correlation among variables and time series. Second, temporal convolution networks with parallel processing ability are used to extract multidimensional features for the downstream prediction task. Finally, many potential anomalies are detected by the best threshold. Experiments on real NASA SMAP/MSL spacecraft datasets show the superiority of our proposed model with respect to state-of-the-art methods.

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Data availability

The datasets generated during and analyzed during the current study are available in the telemanom repository, https://github.com/khundman/telemanom.

Notes

  1. The code is available at https://github.com/Lliang97/Spacecraft-Anonamly-Detection.

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Acknowledgements

This research was supported by the National Defense Basic Scientific Research Program of China under Grant No. JCKY2020903B002 and the Natural Science Foundation of China under Grant No. 62276053.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Liang Liu, Ling Tian & Zhao Kang

  2. Beijing Aerospace Institute for Metrology and Measurement Technology, Beijing, China

    Tianqi Wan

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  1. Liang Liu
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Correspondence to Zhao Kang.

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Liu, L., Tian, L., Kang, Z. et al. Spacecraft anomaly detection with attention temporal convolution networks. Neural Comput & Applic 35, 9753–9761 (2023). https://doi.org/10.1007/s00521-023-08213-9

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