一个混合统计机器学习框架,用于评估地磁风暴对MisrSat2卫星电源子系统的影响
Marwa S Mostafa1, Mohammed Abu Bakr Ali1, N Hesham1
1National Authority for Remote Sensing and Space Science (NARSS), 23 Jozif Tito St., Cairo, 11769, Egypt.
Scientific reports
|October 30, 2025
概括
一个混合框架评估了2024年5月的地磁风暴.
科学领域:
- 太空天气对卫星电力系统的影响
- 统计和机器学习方法在航空航天工程中的应用.
背景情况:
- 地磁风暴对卫星运营构成风险.
- 卫星MisrSat-2的动力子系统需要持续监控.
- 之前的研究往往缺乏统计和ML方法的综合分析.
研究的目的:
- 为了评估2024年5月地磁风暴对MisrSat-2卫星动力子系统的影响.
- 开发和验证用于异常检测的混合统计机器学习框架.
- 通过强大的诊断来确保任务保证.
主要方法:
- 整合了多层次的统计方法 (CUSUM,z-score,基于事件的分析) 与机器学习 (专家混合).
- 采用Welch的t测试,引导和Benjamini-Hochberg错误发现率 (BH-FDR) 来进行统计验证.
- 使用EQUFLUX进行辐射降解建模,并比较ML模型性能 (R2,MAE).
主要成果:
- 在风暴期间识别了太阳能阵列中的适度电流偏差 (面板-1上的13个事件,面板-2上的17个事件),在设计公差范围内 (<4%).
- 电池子系统有效缓冲波动,保持总线完整性.
- 专家混合模型实现了高预测准确度 (R2=0.921,MAE=0.063 A),验证了统计发现.
结论:
- 混合框架成功检测并验证了由地磁风暴引起的轻微电力子系统异常.
- 导航卫星-2动力子系统表现出弹性,没有可测量的辐射驱动的退化.
- 拟议的框架为太空环境中的异常诊断和任务保证提供了一个可扩展的解决方案.
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