上海航天（中英文）2025，Vol.42Issue(2)：157-165,9.DOI:10.19328/j.cnki.2096-8655.2025.02.015
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
摘要
Abstract
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Key words
servo system anomaly detection/multivariate time series/streaming data/concept drift/catastrophic forgetting分类
电子信息工程引用本文复制引用
陈晓,王赞,路辉..基于生成式循环网络的伺服系统在线异常检测[J].上海航天（中英文）,2025,42(2):157-165,9.基金项目
国家自然科学基金资助项目(62371030) （62371030）
