Multiple in-space experiments with deep learning have shown promising results in applying deep neural networks for automation of satellite fault detection tasks. However, the deployment of such neural networks on small satellites with low-power onboard computers is hindered by th
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Multiple in-space experiments with deep learning have shown promising results in applying deep neural networks for automation of satellite fault detection tasks. However, the deployment of such neural networks on small satellites with low-power onboard computers is hindered by the highly limited computational resources of these devices. In this thesis, an effort has been made to create a lightweight neural network solution for satellite temperature monitoring, to explore whether such networks can improve the quality of fault detection even when deployed on low-power devices. The designed neural network solution tested on the MSP432P401R microcontroller uses only 29.5 kB of RAM and 65.2 kB of flash memory and can detect small-sized deviations in the satellite temperature sensor readings long before they grow into anomalies that exceed the safe operating range.
