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.

本研究提出了一种新颖的方法，它利用具有建模复杂多维分布能力的基于物理模拟的Real NVP神经网络，并结合基于传感器数据排列的自监督任务，旨在增强卫星多变量时间序列中的故障检测，实验证明各种设置中都显著提高了性能，特别是仅使用自监督损失获得了最佳结果，表明其在指导网络提取与故障检测相关的特征方面的有效性，这项研究为改善空间系统中的故障检测提供了有希望的方向，并值得在其他数据集和应用中进一步探索。

卫星多变量时间序列故障检测的自监督任务