@inproceedings{8fcbdc5cb51e499cb875dee8d1c24c30,
title = "Satellite Telemetry Anomaly Detection Based on Gradient Boosting Regression with Feature Selection",
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, Feature selection, Gradient Boosting, Satellite",
author = "Zhidong Li and Bo Sun and Weihua Jin and Lei Zhang and Rongzheng Luo",
note = "Publisher Copyright: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 11th EAI International Conference on Wireless and Satellite Systems, WiSATS 2020 ; Conference date: 17-09-2020 Through 18-09-2020",
year = "2021",
doi = "10.1007/978-3-030-69072-4\_18",
language = "英语",
isbn = "9783030690717",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "210--219",
editor = "Qihui Wu and Kanglian Zhao and Xiaojin Ding",
booktitle = "Wireless and Satellite Systems - 11th EAI International Conference, WiSATS 2020, Proceedings",
address = "德国",
}