使用机器学习探索EgyptSat-1的太空天气条件和功率性能之间的关系
Dalia Elfiky1, Mohammed Abu Bakr Ali1, Marwa S Mostafa2
1National Authority for Remote Sensing and Space Science (NARSS), 23 Jozif Tito St, Cairo, 11769, Egypt.
Scientific reports
|December 12, 2025
概括
这项研究引入了一种新的工作流程,使用机器学习来量化太空天气对卫星动力系统的影响,揭示太阳事件和卫星异常之间的潜在关系. 随机森林模型显示出强大的预测性能,有助于提高卫星弹性和监测策略.
科学领域:
- 太空天气 太空天气
- 卫星工程 卫星工程
- 机器学习 机器学习
背景情况:
- 卫星至关重要,但太空天气对它们的动力子系统构成风险.
- 现有的研究往往缺乏综合的,数据驱动的方法来分析太空天气对卫星遥测的复杂影响.
- 了解这些影响对于卫星的可靠性和防止故障至关重要.
研究的目的:
- 开发和验证一种新的数据驱动工作流程,用于定量评估太空天气对卫星电源子系统的影响.
- 研究关键空间天气指标与卫星功率遥测 (电压,电流,温度) 之间的关系.
- 通过先进的机器学习技术,增强异常检测和提高卫星弹性.
主要方法:
- 一个包含数据预处理的四阶段工作流,一个两阶段的非线性特征选择 (RBM和相互信息),六个机器学习模型 (CNN,LSTM,RF等). ) 和异常检测.
- 使用受限制的博尔茨曼机器 (RBM) 和相互信息 (MI) 进行特征选择.
- 采用机器学习模型,包括随机森林 (RF),CNN和LSTM,用于动态系统行为分析和异常检测.
主要成果:
- 随机森林模型对卫星电源子系统参数 (NN1_电压和TBS1_电流) 显示出强大的预测准确性.
- 检测到的异常显示了与银河宇宙射线 (GCR) 干扰 (31%) 和P10质子事件 (27%) 的显著时间巧合率.
- 统计验证证实了显著的p值,表明太空天气事件和卫星异常之间存在潜在的关系.
结论:
- 开发的工作流程有效量化了太空天气对卫星动力系统的影响,展示了机器学习在这个领域的潜力.
- 这些发现有助于改进卫星的监测和弹性战略,这些战略对于理解像EgyptSat-1这样的无法解释的故障很重要.
- 建议对多个卫星数据集进行进一步验证,以扩大拟议方法的适用性.
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