A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series
<|reference_start|>A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series: In the space sector, due to environmental conditions and restricted accessibility, robust fault detection methods are imperative for ensuring mission success and safeguarding valuable assets. This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions, augmented with a self-supervised task based on sensors' data permutation. It focuses on enhancing fault detection within the satellite multivariate time series. The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Results indicate significant performance improvements across all settings. In particular, employing only the self-supervised loss yields the best overall results, suggesting its efficacy in guiding the network to extract relevant features for fault detection. This study presents a promising direction for improving fault detection in space systems and warrants further exploration in other datasets and applications.<|reference_end|>
@article{cena2024a,
    title={A Self-Supervised Task for Fault Detection in Satellite Multivariate
  Time Series},
    author={Carlo Cena, Silvia Bucci, Alessandro Balossino, Marcello Chiaberge},
    journal={arXiv preprint arXiv:2407.02861},
    year={2024},
    doi={10.5281/zenodo.13885595},
    archivePrefix={arXiv},
    eprint={2407.02861},
    primaryClass={cs.LG cs.RO}
}