Neural nets use for satellite telemetry analysis in application for Kazstsat mission

Arman Bekembayev, Rustem Takhanov, Vladimir Ten, Manap Shymyr

Research output: Contribution to journalConference articlepeer-review

Abstract

KazSTSAT mission was launched on the 3rd December 2018 and so far the results are exceeding the pre-launch expectations. As with any serious mission there is a need for short and long term analysis of the telemetry, which is traditionally achieved by qualified operators looking at the data. A ML tool analyzing vast scope of information obtained from a spacecraft would be very useful for KazSTSAT and other missions. The research has the aim to make use of automatic self-learning machines that can predict future states of the space system based on the archived and real-time telemetry and telecommand data. The expected output is the deep learning software application that can be widely used for: • Failure Detection Isolation and Recovery (FDIR) analysis as the real-word modelling environment; • System functional tests as the additional verification method of the Concept of Operations; • Spacecraft operators training to predict final spacecraft subsystems states in case of the intentionally induced anomalies; • On-orbit commissioning and operations to reduce the risks of mission critical anomalies. The paper provides an overview of the application development steps, the difficulties encountered during the design and implementation on real world telemetry data.

Original languageEnglish
Article numberIAC-19_B4_6A_10_x54983
JournalProceedings of the International Astronautical Congress, IAC
Volume2019-October
Publication statusPublished - Jan 1 2019
Event70th International Astronautical Congress, IAC 2019 - Washington, United States
Duration: Oct 21 2019Oct 25 2019

Keywords

  • Machine Learning
  • Neural Nets
  • Telemetry processing

ASJC Scopus subject areas

  • Aerospace Engineering
  • Astronomy and Astrophysics
  • Space and Planetary Science

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