  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.
