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距离相关系数融合GPR模型的卫星异常检测方法

孙宇豪 李国通 张鸽

孙宇豪, 李国通, 张鸽等 . 距离相关系数融合GPR模型的卫星异常检测方法[J]. 北京航空航天大学学报, 2021, 47(4): 844-852. doi: 10.13700/j.bh.1001-5965.2020.0041
引用本文: 孙宇豪, 李国通, 张鸽等 . 距离相关系数融合GPR模型的卫星异常检测方法[J]. 北京航空航天大学学报, 2021, 47(4): 844-852. doi: 10.13700/j.bh.1001-5965.2020.0041
SUN Yuhao, LI Guotong, ZHANG Geet al. A satellite anomaly detection method based on distance correlation coefficient and GPR model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(4): 844-852. doi: 10.13700/j.bh.1001-5965.2020.0041(in Chinese)
Citation: SUN Yuhao, LI Guotong, ZHANG Geet al. A satellite anomaly detection method based on distance correlation coefficient and GPR model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(4): 844-852. doi: 10.13700/j.bh.1001-5965.2020.0041(in Chinese)

距离相关系数融合GPR模型的卫星异常检测方法

doi: 10.13700/j.bh.1001-5965.2020.0041
基金项目: 

上海市科学技术委员会科研计划 17DZ1100700

详细信息
    作者简介:

    孙宇豪  女, 硕士研究生。主要研究方向: 卫星异常检测技术

    李国通  男, 博士, 研究员, 博士生导师。主要研究方向: 卫星导航与定位技术、信号检测和识别技术

    张鸽   男, 博士研究生。主要研究方向: 卫星自主健康管理技术

    通讯作者:

    李国通, E-mail: ligt@microsate.com

  • 中图分类号: V557+.3;TN911.71

A satellite anomaly detection method based on distance correlation coefficient and GPR model

Funds: 

Scientific Research Plan of Shanghai Science and Technology Commission 17DZ1100700

More Information
  • 摘要:

    卫星在轨运行期间,遥测数据表现形式通常为多维时间序列。高斯过程回归(GPR)模型可以为重要的遥测参数提供动态门限,及时发现隐藏在工程阈值内的故障征兆,但是高维卫星数据使得GPR模型具有局限性。因此,为获取与多个遥测参数相关的动态门限,在GPR模型的基础上,融合距离相关系数对预测变量进行选择,减少信息冗余和计算量,提高模型的可解释性,并估计模型的泛化误差以设置更合理的预测区间,提高模型的泛化能力,检测数据流的持续异常。对实际在轨卫星数据进行仿真实验,验证了距离相关系数融合GPR模型的卫星异常检测方法可以在卫星故障早期检测到数据异常,而且提高了模型的预测性能,降低了虚警率。

     

  • 图 1  过拟合模型的检测结果

    Figure 1.  Detection results of overfitting model

    图 2  基于DC-GPR模型的异常检测流程

    Figure 2.  Flowchart of anomaly detection based on DC-GPR model

    图 3  故障相关单机的遥测参数

    Figure 3.  Telemetry parameters of a fault-related machine

    图 4  不同遥测参数与响应变量间的距离相关系数

    Figure 4.  Distance correlation coefficient between each telemetry parameter and response variable

    图 5  不同模型的检测结果

    Figure 5.  Detection results of different models

    图 6  DC-GPR模型95%置信区间检测结果

    Figure 6.  Detection results of DC-GPR 95% confidence interval

    图 7  不同模型的性能指标对比

    Figure 7.  Comparison of performance indices of different models

    图 8  异常时不同遥测参数与响应变量间的距离相关系数

    Figure 8.  Distance correlation coefficient between each telemetry parameter and response variable during anomalies

    表  1  相关程度度量

    Table  1.   Relevance measure

    相关性 距离相关系数
    极强相关 0.8~1.0
    强相关 0.6~0.8
    中相关 0.4~0.6
    弱相关 0.2~0.4
    极弱或无相关 0~0.2
    下载: 导出CSV

    表  2  不同模型的检测评估

    Table  2.   Detection evaluation of different models

    模型 DR/% FA/% MA/%
    DC-GPR 86.67 5.41 0
    DC-GPR(95%置信区间) 52.94 19.51 0
    GPR 60 14.63 0
    RV-GPR 86.67 11.76 57.14
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-17
  • 录用日期:  2020-04-17
  • 网络出版日期:  2021-04-20

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