用分层集成方法检测卫星遥测数据中的异常
A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
- 结合形状特征与统计特征，分通道建模后融合
- 在ESA基准数据集上表现优异，能发现细微异常
- 适合航天器状态监控与故障预警场景
提出一种分层集成流水线，用于处理欧洲空间局（ESA）提供的多变量遥测数据异常检测问题。方法融合基于形状的特征提取与统计特征提取，采用逐通道建模、通道内堆叠和跨通道聚合的分层结构。通过时间序列交叉验证与两级掩码策略训练和验证，有效防止信息泄露。在欧洲空间局异常检测基准挑战赛（ESA-ADB）上的结果表明，该方法具有强泛化能力，证明了分层建模在真实卫星遥测数据中检测细微异常的有效性。
原文摘要 · Abstract (English)
A hierarchical ensemble pipeline is introduced to address anomaly detection in multivariate telemetry data provided by European Space Agency (ESA). The method integrates shapelet-based and statistical feature extraction, per-channel modeling, intra-channel stacking, and a final cross-channel aggregation. The pipeline is trained and validated using time-series cross-validation and two-level masking strategies to prevent information leakage. Results on the European Space Agency Anomaly Detection Benchmark (ESA-ADB) challenge demonstrate strong generalization, highlighting the effectiveness of hierarchical modeling in detecting subtle anomalies in realistic satellite telemetry.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。