Causal Structure Analysis for Telemetry Anomaly Detection in Spacecraft Systems

Abstract

Spacecraft telemetry anomaly detection is critical for mission success and safety. This research applies causal inference methods to understand the underlying causal structure in spacecraft telemetry data. We developed graphical models that can identify root causes of anomalies, enabling more effective predictive maintenance and mission planning strategies.