data mining for safer spacecrafts - ku leuven · data mining for safer spacecrafts . anomaly...
TRANSCRIPT
Thesis proposal form
Thesis proposed by: ESAT-PSI, in collaboration with the Institute of Astronomy
Data Mining for Safer Spacecrafts Anomaly detection using machine learning techniques
GUIDANCE
• For more information, please contact: [email protected]
• Promotor(s):
Prof. dr. Matthew Blaschko
• Daily advisors:
Dr. Joris De Ridder
Dr. Pierre Royer
• Number of students 1
CONTEXT
• Modern spacecrafts are complex systems, made of partially independent subsystems. • Thousands of telemetry parameters are monitored onboard and downlinked to watch over the
health of the spacecraft. • This telemetry is used to make near-real time decisions by the spacecraft operator on how to
proceed with the operations or to decide which safeguard procedures to undertake. • It is impossible to manually go through the massive amounts of telemetry data to sift for
anomalies to investigate the health status of the spacecraft. More intelligent methods need to be developed.
• We collaborate with the European Space Agency (ESA) to design novel data-centric analysis methods that lead the spacecraft operators towards a faster and more exhaustive detection of on-board anomalies and guide them efficiently through the massive amounts of data towards their root cause.
GOAL
The goal of the project is to develop intelligent machine learning methods to derive the underlying correlations between telemetry parameters, and turn these correlations into reliable diagnostics that are useful for spacecraft operators to identify anomalous behavior of one or more spacecraft subsystems.
METHODOLOGY
• The student will get experience with a variety of methods, including correlation techniques, graphs, likelihood estimation, neural networks etc.
• Any methodology developed will be tested on a database of thousands of telemetry series of the ESA space missions Venus Express, Mars Express, and/or Gaia.
PROFILE/REQUIRED SKILLS
• You have a keen interest “big data” applications, and you want to have hands-on experience with modern statistical and machine learning techniques.
• You are fascinated by space applications and want to get your hands on actual spacecraft telemetry.
Thesis proposal form
• You already know or are ready to learn the computer language Python or Julia and use it to implement the methodology (using existing libraries wherever possible).
REFERENCES