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Health monitoring is performed on CNES spacecraft using two complementary methods: an  utomatic Out-Of-Limits (OOL) checking executed on a set of  critical parameters after each new telemetry  reception, and a monthly monitoring  of statistical features (daily minimum, mean and  maximum) of another set of parameters. In this paper we  present  the limitations of this monitoring  system and we introduce an innovative anomaly detection method based on machine-learning  algorithms, developed during a collaborative R&D action between CNES and TESA  (TElecommunications for Space and  Aeronautics). This method has been prototyped  and  has shown encouraging results  regarding  its  ability to detect actual anomalies that had slipped  through the existing monitoring net. An operational-ready software implementing this  method, NOSTRADAMUS, has been developed in order to further evaluate the interest of this new type of surveillance, and to consolidate the settings  proposed after the R&D action. The lessons learned from the operational assessment of this system for the routine surveillance of CNES spacecraft are also presented in this paper

Improving Spacecraft Health Monitoring with Automatic Anomaly Detection Techniques