  The Mars Express (MEX) spacecraft has been orbiting Mars since 2004. The
operators need to constantly monitor its behavior and handle sporadic
deviations (outliers) from the expected patterns of measurements of quantities
that the satellite is sending to Earth. In this paper, we analyze the patterns
of the electrical power consumption of MEX's thermal subsystem, that maintains
the spacecraft's temperature at the desired level. The consumption is not
constant, but should be roughly periodic in the short term, with the period
that corresponds to one orbit around Mars. By using long short-term memory
neural networks, we show that the consumption pattern is more irregular than
expected, and successfully detect such irregularities, opening possibility for
automatic outlier detection on MEX in the future.
