全球定位系统2026，Vol.51Issue(2)：40-49,10.DOI:10.12265/j.gnss.2026033
基于特征优选和深度Q网络的深空探测器姿控系统自主故障诊断研究
Research on autonomous fault diagnosis of deep space probes based on feature selection and deep Q-network
摘要
Abstract
Aiming at the problems of imbalance in sensor samples and low autonomy of fault diagnosis for deep space probes'attitude control systems,an autonomous fault diagnosis method is proposed based on feature selection and deep Q-network(DQN).In this method,sensor signals are first converted into time-domain and wavelet packet energy feature parameters.A distance-based evaluation criterion is then constructed for feature selection to obtain a more representative feature subset.Subsequently,the fault diagnosis issue is modeled using the imbalanced classification Markov decision process,and a specialized reward function is designed for the imbalanced samples,enabling DQN to focus more on minority-class samples.Finally,through training conducted via environmental interactions,DQN can autonomously learn the optimal diagnostic strategy.The simulation results demonstrate that the proposed method achieves high diagnostic accuracy and stability on multiple imbalanced datasets.关键词
深空探测器/故障诊断/不平衡样本/特征优选/深度Q网络(DQN)Key words
deep space probe/fault diagnosis/imbalanced samples/feature selection/deep Q-network(DQN)分类
航空航天引用本文复制引用
袁馨,钱振,于牧野,董天舒,符方舟..基于特征优选和深度Q网络的深空探测器姿控系统自主故障诊断研究[J].全球定位系统,2026,51(2):40-49,10.基金项目
国家自然科学基金(U22B6001,62473389,62388101) （U22B6001,62473389,62388101）
广东省自然科学基金(2025B1515020097,2024A1515011730) （2025B1515020097,2024A1515011730）
