Neural Nets use for satellite telemetry analysis in application for KazSTSAT mission
- Paper number
IAC-19,B4,6A,10,x54983
- Author
Mr. Arman Bekembayev, Kazakhstan, Ghalam LLP
- Coauthor
Dr. Vladimir Ten, Kazakhstan, Ghalam LLP
- Coauthor
Prof. Rustem Takhanov, Kazakhstan
- Coauthor
Mr. Manap Shymyr, Kazakhstan, Nazarbayev University
- Year
2019
- Abstract
\begin{document} KazSTSAT mission was launched on the 3rd December 2018 and so far looking good with some minor anomalies encountered during commissioning phase. But there is a need in a tool analyzing vast scope of information obtaned by KazSTSAT spacecraft operation center. 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.\newline The expected output is the deep learning software application that can be widely used for: \begin{itemize}\item Failure Detection Isolation and Recovery (FDIR) analysis as the real-word modelling environment;\end{itemize} \begin{itemize}\item System functional tests as the additional verification method of the Concept of Operations; \end{itemize} \begin{itemize}\item Spacecraft operators training to predict final spacecraft subsystems states in case of the intentionally induced anomalies; \end{itemize} \begin{itemize}\item On-orbit commissioning and operations to reduce the risks of fatal mission anomalies. \end{itemize} \newline The paper provides an overview of the application development steps, the difficulties encountered during the design and implementation on real world telemetry data. \end{document}
- Abstract document
- Manuscript document
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