In the framework of Failure Detection, Isolation and Recovery (FDIR) on spacecraft, new AI-based approaches are emerging in the state of the art to overcome the limitations commonly imposed by traditional threshold checking. The present research aims at characterizing two different approaches to the problem of stuck values detection in multivariate time series coming from spacecraft attitude sensors. The analysis reveals the performance differences in the two approaches, while commenting on their interpretability and generalization to different scenarios.
@article{arxiv.2409.17841,
title = {Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors},
author = {R. Gallon and F. Schiemenz and A. Krstova and A. Menicucci and E. Gill},
journal= {arXiv preprint arXiv:2409.17841},
year = {2024}
}