中国空间科学技术（中英文）2025，Vol.45Issue(1)：46-58,13.DOI:10.16708/j.cnki.1000-758X.2025.0005
基于数字孪生和机器学习的卫星未知故障检测方法
Detecting satellite unknown fault patterns using digital twin and machine learning
摘要
Abstract
Traditional satellite fault diagnosis methods and existing data-driven diagnosis methods both face challenges in identifying unknown faults that differ from known fault types,resulting in lower reliability and safety.To address the problem,a fault diagnosis and unknown fault detection method based on satellite digital twin and machine learning models is proposed.Firstly,various types of fault-simulated data are generated using satellite digital twin,and the fidelity of digital twin data are validated using XGBoost and real satellite fault samples,achieving the diagnosis of known fault types.On this basis,considering that existing methods cannot identify the occurrence of unknown fault types precisely,an out-of-distribution detection model Con-DAGMM is proposed,which is trained on normal data and known fault data to provide warnings for unknown fault.Experiments are conducted using digital twin data and satellite real fault data.The experimental results demonstrate that the proposed method achieves high fault diagnosis accuracy with an average accuracy of 98.8%on the test data.Furthermore,Con-DAGMM achieve high-performance unknown fault detection,outperforming Deep-SVDD and other comparison methods in precision,recall and F1 scores.The results indicate that satellite digital twin can overcome the scarcity of fault samples in satellite historical data,and the out-of-distribution detection approach can be successfully applied to warning of satellite unknown faults,enhancing the satellite's safety and reliability.关键词
卫星控制系统/未知故障检测/故障诊断/数字孪生/机器学习/分布外检测Key words
satellite control system/unknown fault detection/fault diagnosis/digital twin/machine learning/out-of-distribution detection分类
航空航天引用本文复制引用
沈英龙,蔡君亮,林佳伟,杨帆..基于数字孪生和机器学习的卫星未知故障检测方法[J].中国空间科学技术（中英文）,2025,45(1):46-58,13.基金项目
国家自然科学基金(62173282) （62173282）
厦门市自然科学基金(3502Z20227180) （3502Z20227180）
