Physics-Informed Real NVP for Satellite Power System Fault Detection
空间环境所提出的独特挑战，其特点为极端条件和有限的可用性，导致需要开发出健壮和可靠的故障检测技术来识别和预防卫星故障。空间 sector 中的故障检测方法需要确保任务成功并保护有价值资产。在本文中，我们提出了一个基于人工智能 (AI) 的故障检测方法，并评估了其在 ADAPT(高级诊断和预测测试台) 数据集上的性能。我们的研究重点是应用物理引导 (PI) 实值非体积保留 (Real NVP) 模型在空间系统中的故障检测。这种方法与其他 AI 方法，如门控循环单元 (GRU) 和基于编码器的技术进行了系统比较。结果表明，我们的物理引导方法超越了现有的故障检测方法，证明了它适用于解决卫星 EPS 子系统故障这一独特挑战。此外，我们还揭示了 AI 模型中物理引导损失的竞争优势，以满足特定的空间需求，即健壮性、可靠性和电力限制，这对 space 探索和卫星任务至关重要。
The unique challenges posed by the space environment, characterized by extreme conditions and limited accessibility, raise the need for robust and reliable techniques to identify and prevent satellite faults. Fault detection methods in the space sector are required to ensure mission success and to protect valuable assets. In this context, this paper proposes an Artificial Intelligence (AI) based fault detection methodology and evaluates its performance on ADAPT (Advanced Diagnostics and Prognostics Testbed), an Electrical Power System (EPS) dataset, crafted in laboratory by NASA. Our study focuses on the application of a physics-informed (PI) real-valued non-volume preserving (Real NVP) model for fault detection in space systems. The efficacy of this method is systematically compared against other AI approaches such as Gated Recurrent Unit (GRU) and Autoencoder-based techniques. Results show that our physics-informed approach outperforms existing methods of fault detection, demonstrating its suitability for addressing the unique challenges of satellite EPS sub-system faults. Furthermore, we unveil the competitive advantage of physics-informed loss in AI models to address specific space needs, namely robustness, reliability, and power constraints, crucial for space exploration and satellite missions.
https://arxiv.org/abs/2405.17339
https://arxiv.org/pdf/2405.17339.pdf