摘要
基于遥测数据的异常检测是卫星在轨运维管理的关键技术。但现有方法大多仅采用正常样本建立模型,异常检测结果对判读阈值敏感、虚警率较高。对此本文提出基于对比序列重构的卫星遥测数据异常检测方法,充分利用有限异常先验增强异常检测中正常、异常样本差异。先基于变分自编码器提取遥测数据时序演化特征,引入对比学习方法建立对异常、正常数据差异化输出的编码器,再用大量正常数据进一步训练整个模型实现对正常数据的精准重构形成对异常数据敏感的时序数据重构模型,再基于核密度估计方法学习异常判读阈值进一步提升异常检出率。在真实卫星遥测数据上验证表明所提方法能有效降低异常检测的虚警率(均低于0.002)并保持较高的检出率具备良好的实际应用水平。
Anomaly detection based on telemetry data is a key technology for the on-orbit operation and maintenance management of satellite.However,most of the existing methods only use normal samples to build models,while the anomaly detection results are sensitive to the detection threshold,resulting in a high false positive rate.To address this problem,this paper proposes an anomaly detection method based on contrastive time-series reconstruction satellite of telemetry data,which makes full use of the prior knowledge of limited abnormal telemetry samples to enhance the differences between normal and abnormal samples.First,variational autoencoders is used to extract the time-series evolutionary characteristics of telemetry data,specifically the contrastive learning method is introduced to establish an encoder with differentiated outputs of abnormal and normal telemetry data,which uses a large amount of normal telemetry data to further train the whole model to achieve precise time-series reconstruction of normal telemetry data and form a time-series data reconstruction model sensitive to abnormal data.Then the anomaly detection threshold of satellite telemetry data is deduced based on the kernel density estimation method to further improve the detection rate of abnormal samples.Experimental verification was conducted using real satellite telemetry data and the results show that the proposed method can effectively use historical abnormal samples to establish an anomaly detection model,effectively reduce the false positive rate(all below 0.002)of anomaly detection and maintain a high detection rate at the same time,keeping a good practical application level.
作者
李桢煜
宋宇晨
彭喜元
刘大同
Li Zhenyu;Song Yuchen;Peng Xiyuan;Liu Datong(School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150080,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第4期17-26,共10页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(62201177)项目
黑龙江省自然基金优秀青年项目(YQ2023F006)资助。
