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Conference Papers Year : 2021

Machine learning as an alternative to thresholding for space radiation high current event detection

Abstract

The space environment is known to be the seat of radiation of different kinds to which satellites in orbit are subjected. These include cosmic rays that come from stars and radiation belts that come from the Earth magnetic field. The impact of radiation on electronic components results in anomalies called "Single Event Effects" which can lead to the destruction of equipment. Various protection methods exist, like hardening of components or satellite shielding, but they are often costly and/or difficult to implement. This is why space designers try to circumvent these processes by an efficient software protection method. This paper reports a set of experiments based on machine learning tools that will provide the basis to design and develop an anomaly detection method for Single Event Effects. The data sets that were used are issued from emulated radiations obtained by laser tests on a SAM3X microcontroller, complemented by data obtained by simulation.
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Dates and versions

hal-03331030 , version 1 (01-09-2021)

Identifiers

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Adrien Dorise, Corinne Alonso, Audine Subias, Louise Travé-Massuyès, Leny Baczkowski, et al.. Machine learning as an alternative to thresholding for space radiation high current event detection. Radiation and its Effects on Components and Systems - RADECS 2021, Sep 2021, Vienna, Austria. ⟨10.1109/RADECS53308.2021.9954582⟩. ⟨hal-03331030⟩
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