用多通道CNN实时检测航天器传感器故障，提升故障发现能力与响应速度。
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
- 设计多通道CNN模型，同时监测多个传感器的异常信号。
- 在自定义数据集上实现高精度故障检测，触发恢复动作响应及时。
- 专为星载系统优化，适合需要高可靠性的航天器故障诊断场景。
传统卫星上的异常检测依赖于基于阈值的单变量信号监控，遵循欧洲空间标准化协会（ECSS）标准，但存在检测范围有限的问题。近年来，基于人工智能的故障检测、隔离与恢复（FDIR）方案展现出突破传统方法局限的潜力，可扩大故障检测范围并缩短响应时间。本文提出一种新方法，针对探索小行星等太阳系小天体的类无人机航天器，利用多通道卷积神经网络（CNN）对加速度计与惯性测量单元中的卡死值故障进行多目标分类，并独立识别各传感器故障。研究特别关注算法在星载FDIR系统中的兼容性，推进该技术从实验阶段迈向在轨验证。提出了集成方法，使网络能有效检测异常并在系统层面触发恢复动作。通过自定义的检测指标和系统性能指标评估，结果表明该算法在执行FDIR任务中表现优异。
原文摘要 · Abstract (English)
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.
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