Abstract
Spacecraft anomaly detection is critical for mission safety, yet deploying sophisticated models on-board presents significant challenges due to hardware constraints. This paper investigates three approaches for spacecraft telemetry anomaly detection -- forecasting & threshold, direct classification, and image classification -- and optimizes them for edge deployment using multi-objective neural architecture optimization on the European Space Agency Anomaly Dataset. Our baseline experiments demonstrate that forecasting & threshold achieves superior detection performance (92.7% Corrected Event-wise F0.5-score (CEF0.5)) [1] compared to alternatives. Through Pareto-optimal architecture optimization, we dramatically reduced computational requirements while maintaining capabilities -- the optimized forecasting & threshold model preserved 88.8% CEF0.5 while reducing RAM usage by 97.1% to just 59 KB and operations by 99.4%. Analysis of deployment viability shows our optimized models require just 0.36-6.25% of CubeSat RAM, making on-board anomaly detection practical even on highly constrained hardware. This research demonstrates that sophisticated anomaly detection capabilities can be successfully deployed within spacecraft edge computing constraints, providing near-instantaneous detection without exceeding hardware limitations or compromising mission safety.
Abstract (translated)
航天器异常检测对任务安全至关重要，然而受硬件限制，在星上部署复杂模型仍面临巨大挑战。本研究针对航天器遥测异常检测，探究了预测与阈值法、直接分类法及图像分类法三种方案，并利用多目标神经架构优化技术，在欧洲空间局异常数据集上优化其边缘部署性能。基线实验表明，预测与阈值法的检测效果最优（修正事件级F0.5分数达92.7%）[1]。通过帕累托最优架构优化，我们在保持检测能力的同时大幅降低计算需求——优化后的预测与阈值模型在保留88.8%修正事件级F0.5分数的前提下，将内存占用减少97.1%至仅59 KB，运算量降低99.4%。部署可行性分析显示，优化后模型仅需0.36%-6.25%的立方星内存，使星上异常检测即使在高度受限的硬件上也可行。本研究证明，复杂的异常检测能力可成功部署于航天器边缘计算约束内，实现近瞬时检测且不超出硬件限制或损害任务安全。
URL
https://arxiv.org/abs/2603.29375
