Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

被引:1
|
作者
Li, Wenbo [1 ]
Ning, Baoling [2 ]
机构
[1] Beijing Inst Control Engn, Natl Key Lab Sci & Technol Space Intelligent Contr, Beijing 100190, Peoples R China
[2] Heilongjiang Univ, Sch Data Sci & Technol, Harbin 150080, Heilongjiang, Peoples R China
基金
国家重点研发计划;
关键词
Space vehicles; Costs; Anomaly detection; Training; Time series analysis; Telemetry; Filtering;
D O I
10.1109/JAS.2023.123423
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets. Based on a sublinear time filtering method, an efficient algorithm for evaluating the detectabilities is designed. By combining the above techniques, RecAD is proposed for the recommendation of fault detection algorithms. Experimental results over typical datasets show that RecAD can select the detecting algorithm with better performance efficiently and the cost of the recommendation is rather small.
引用
收藏
页码:273 / 275
页数:3
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