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.

翻译：在航天领域，由于环境条件的限制及可访问性的制约，为确保任务成功并保护高价值资产，鲁棒的故障检测方法至关重要。本研究提出一种新颖方法，该方法利用以建模复杂高维分布能力著称的物理信息Real NVP神经网络，并辅以基于传感器数据置换的自监督任务进行增强。该方法着重于提升卫星多元时间序列中的故障检测能力。实验涉及多种配置，包括自监督预训练、多任务学习以及独立的自监督训练。结果表明，在所有设置下均取得了显著的性能提升。特别值得注意的是，仅使用自监督损失即可获得最佳的整体结果，这提示了其在引导网络提取故障检测相关特征方面的有效性。本研究为改进空间系统的故障检测提供了一个有前景的方向，值得在其他数据集和应用中进一步探索。