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Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II

Research Article

Satellite Telemetry Anomaly Detection Based on Gradient Boosting Regression with Feature Selection

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  • @INPROCEEDINGS{10.1007/978-3-030-69072-4_18,
        author={Zhidong Li and Bo Sun and Weihua Jin and Lei Zhang and Rongzheng Luo},
        title={Satellite Telemetry Anomaly Detection Based on Gradient Boosting Regression with Feature Selection},
        proceedings={Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II},
        proceedings_a={WISATS PART 2},
        year={2021},
        month={2},
        keywords={Anomaly detection Satellite Gradient Boosting Feature selection},
        doi={10.1007/978-3-030-69072-4_18}
    }
    
  • Zhidong Li
    Bo Sun
    Weihua Jin
    Lei Zhang
    Rongzheng Luo
    Year: 2021
    Satellite Telemetry Anomaly Detection Based on Gradient Boosting Regression with Feature Selection
    WISATS PART 2
    Springer
    DOI: 10.1007/978-3-030-69072-4_18
Zhidong Li1,*, Bo Sun1, Weihua Jin2, Lei Zhang1, Rongzheng Luo1
  • 1: Beijing Institute of Spacecraft System Engineering
  • 2: Research Center of Satellite Technology, Harbin Institute of Technology
*Contact email: lizhidongcas@163.com

Abstract

A data-driven satellite telemetry data anomaly detection method is proposed. The gradient boosting regression algorithm combined with feature selection, including feature scoring and recursive lowest-score feature elimination, can automatically mine the correlative telemetry variables through iterations and establish a nonlinear regression model for their functional association, which can be used as a health baseline for anomaly detection of telemetry data. This method requires no expert to specify correlative telemetry variables based on domain knowledge beforehand. It has the advantage of self-adaption for satellite operating conditions, which can overcome the problem of functional association altering under different operating conditions caused by orbit or sunshine condition changes. The validity and effectiveness of the method is verified by the telemetry data of the power subsystem.

Keywords
Anomaly detection Satellite Gradient Boosting Feature selection
Published
2021-02-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-69072-4_18
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