A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series

TL;DR

This paper introduces a physics-informed neural network approach with self-supervised learning for improved fault detection in satellite multivariate time series, demonstrating significant performance gains.

Contribution

It proposes a novel self-supervised task combined with Physics-Informed Real NVP networks for fault detection, enhancing feature extraction and detection accuracy.

Findings

Self-supervised training outperforms other configurations.

Pre-training improves fault detection performance.

Self-supervised loss alone yields the best results.

Abstract

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.