Discovering outliers in the Mars Express thermal power consumption patterns

TL;DR

This paper analyzes the thermal power consumption patterns of the Mars Express spacecraft, using neural networks to detect irregularities and outliers, which could enable automated anomaly detection in spacecraft systems.

Contribution

The study applies LSTM neural networks to identify irregular power consumption patterns in Mars Express, demonstrating the potential for automatic outlier detection in spacecraft monitoring.

Findings

LSTM models effectively detect irregular power consumption patterns.

Power consumption patterns are more irregular than initially expected.

Automatic outlier detection can improve spacecraft health monitoring.

Abstract

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.