Satellites should have high reliability because they are required to operate autonomously, while performing a given mission. To realize this, it is necessary to use heritage parts or include redundancy. However, for nanosatellites, where it is difficult to include redundancy due to volume and weight limitations, fault management becomes crucial. In this study, a new method based on deep learning is proposed for detecting and identifying the faults in the reaction wheel, which is one of the satellite actuators. A deep learning model is applied to learn the fault and fault type using the residual between the measured attitude data and the estimated attitude date. In this study, it is assumed that three reaction wheels are installed, and fault detection is designed accordingly. The proposed model enables the satellite to detect faults autonomously, even when it is not communicating with the ground station, and is expected to be highly beneficial for the autonomous operation of the mega-constellation mission using nanosatellite that is to be activated in future.

中文翻译：

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基于深度学习的纳米卫星姿态控制系统故障检测与识别算法的开发

卫星应该具有高可靠性，因为它们需要在执行特定任务时自主运行。为了实现这一点，有必要使用传统部件或包括冗余。然而，对于纳米卫星来说，由于体积和重量的限制，很难包括冗余，故障管理变得至关重要。在这项研究中，提出了一种基于深度学习的新方法来检测和识别作为卫星执行器之一的反作用轮的故障。应用深度学习模型使用测量姿态数据和估计姿态数据之间的残差来学习故障和故障类型。在这项研究中，假设安装了三个反作用轮，并相应地设计了故障检测。所提出的模型使卫星能够自主检测故障，