Health-State Baseline Modeling of Spacecraft Telemetry Parameters Using HPM-Former

Telemetry data provide essential information on a spacecraft’s in-orbit operating state and its evolution, supporting system health-state awareness, assessment, and judgment. However, existing data-driven health-state modeling methods may provide unstable representations of healthy operating patterns and have difficulty distinguishing local disturbances from state deviations in complex spacecraft telemetry. To address these problems, this paper proposes a Health Prototype Memory Transformer (HPM-Former) for telemetry health-state baseline modeling. HPM-Former uses convolutional structures to extract local fluctuation features and a Transformer to model global temporal dependencies among multivariate telemetry parameters. Health-memory prototypes learn representative healthy patterns under normal operating conditions, enabling stable representations of healthy spacecraft operation. A health-consistency deviation is constructed from reconstruction deviation, health-prototype matching, and uncertainty in memory addressing to characterize input deviation from the health-state baseline, thereby supporting comprehensive state assessment. Experiments on the European Space Agency Anomalies Dataset (ESA-AD) show that HPM-Former stably represents healthy operating patterns and improves the reliability of current-state assessment against the health-state baseline. It outperforms the comparison methods in corrected event-wise F0.5 and event-wise alarming precision, providing an effective data-driven approach for operational-state assessment of complex spacecraft.