用于航天器姿态传感器实时多变量时间序列故障检测的卷积神经网络设计与评估
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
AI总结：
本文提出一种基于多通道卷积神经网络（CNN）的新方法，用于检测无人机式航天器姿态传感器中的卡死值故障，并通过自定义指标验证了其在星载FDIR系统中的高检测性能与触发恢复能力。
摘要：
卫星上的传统异常检测技术基于可靠但有限的阈值机制，这些机制旨在监测单变量信号并根据特定的欧洲航天标准化合作（ECSS）标准触发恢复操作。然而，基于人工智能的故障检测、隔离与恢复（FDIR）解决方案近期兴起，有望克服这些标准方法的局限性，扩大可检测故障的范围并缩短响应时间。本文提出了一种新方法，利用多通道卷积神经网络（CNN）进行多目标分类并独立检测传感器故障，以检测用于探索小太阳系天体（SSSB）的无人机式航天器的加速度计和惯性测量单元中的卡死值。为确保算法与星载FDIR系统的兼容性，本文给予了极大关注，这标志着该技术在轨验证迈出了重要一步，在此之前该技术仍处于实验阶段，直至其鲁棒性得到充分证明。本文提出了一种集成方法，使网络能够有效检测异常并在系统层面触发恢复操作。采用一组自定义的检测和系统指标评估了算法的检测性能及其触发反应的能力，结果表明该算法在执行FDIR任务时表现出卓越的性能。
英文摘要：
Traditional anomaly detection techniques onboard satellites are based on reliable, yet limited, thresholding mechanisms which are designed to monitor univariate signals and trigger recovery actions according to specific European Cooperation for Space Standardization (ECSS) standards. However, Artificial Intelligence-based Fault Detection, Isolation and Recovery (FDIR) solutions have recently raised with the prospect to overcome the limitations of these standard methods, expanding the range of detectable failures and improving response times. This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft for the exploration of Small Solar System Bodies (SSSB), leveraging a multi-channel Convolutional Neural Network (CNN) to perform multi-target classification and independently detect faults in the sensors. Significant attention has been dedicated to ensuring the compatibility of the algorithm within the onboard FDIR system, representing a step forward to the in-orbit validation of a technology that remains experimental until its robustness is thoroughly proven. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level. The detection performances and the capability of the algorithm in reaction triggering are evaluated employing a set of custom-defined detection and system metrics, showing the outstanding performances of the algorithm in performing its FDIR task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
