基于生成式循环网络的伺服系统在线异常检测  

Online Anomaly Detection for Servo Systems with Generative Recurrent Networks

作　　者：陈晓 王赞 路辉[2] CHEN Xiao;WANG Zan;LU Hui(Shanghai Aerospace Control Technology Institute,Shanghai 201109,China;School of Electronics and Information Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]上海航天控制技术研究所,上海201109 [2]北京航空航天大学电子信息工程学院,北京100191

出　　处：《上海航天(中英文)》2025年第2期157-165,共9页Aerospace Shanghai(Chinese&English)

基　　金：国家自然科学基金资助项目(62371030)。

摘　　要：在线异常检测是确保火箭伺服系统正常运行的关键技术。然而,目前大多数研究未考虑模型部署和应用时存在的概念漂移问题,进而影响检测精度。为此,本文提出一种基于生成式循环网络的伺服系统在线异常检测算法。首先,为建立系统输入输出关系模型,提出深层循环神经网络,该网络通过引入多层记忆单元和跳跃连接,来提升其对数据多尺度时空依赖关系的拟合能力;其次,为缓解概念漂移问题,引入在线学习使模型具有持续学习能力,但也带来了灾难性遗忘问题;最后,为缓解灾难性遗忘问题,提出生成式网络,以生成包含历史数据、整体数据分布信息的回顾数据,使模型学习新数据分布的同时,避免遗忘历史数据。结果表明:基于火箭伺服系统所采集的真实运行数据,消融实验和对比实验证明了提出的算法能有效缓解上述问题,并取得较好的异常检测效果。Online anomaly detection is a key technology to ensure the normal operation of rocket servo systems.However,most current methods do not consider the concept drift problem that exists when models are deployed and applied,which in turn affects the detection accuracy.Therefore,based on the generative recurrent networks,this paper proposes an online anomaly detection algorithm for servo systems.First,a deep recurrent neural network is proposed to model the input-output relationship of a servo system.The network introduces multi-layer memory cells and jump connections to improve its capability to fit the multi-scale spatio-temporal dependent properties of the data.Second,to mitigate the conceptual drift problem,online learning is introduced to make the model capable of continuous learning,but it also introduces the problem of catastrophic forgetting.Finally,to mitigate the catastrophic forgetting problem,a generative network is proposed to generate the retrospective data containing information about the overall distribution of historical data.It allows the model to learn new data distributions while avoiding forgetting information about the historical data distributions.Based on the actual rocket servo system operation data,the ablation experiments and comparative experiments are carried out.The results show that the proposed algorithm can effectively address the aforementioned problems and achieve good anomaly detection results.

关 键 词：伺服系统异常检测 多变量时间序列 流数据 概念漂移 灾难性遗忘 

分 类 号：TN277[电子电信—物理电子学]