Machine learning application for spacecraft telemetry analysis and prediction of future anomalies

Paper number

IAC-18,B1,IP,20,x47551

Author

Mr. Arman Bekembayev, Kazakhstan, Ghalam LLP

Year

2018

Abstract

\begin{document}
The research has the aim to make use of automatic self-learning machines that can predict future states of the space systems based on the archived and real-time telemetry and telecommand data obtained by spacecraft operation center. \newline
The research has the output – the machine 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 from KazEOSat-2 (launched in 2014) and KazSTSat (to be launched in Q3 2018) missions.
\end{document}

Abstract document

IAC-18,B1,IP,20,x47551.brief.pdf

Manuscript document

(absent)