Research Article
Satellite Telemetry Anomaly Detection Based on Gradient Boosting Regression with Feature Selection
@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
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.
