Research teams are successfully using advanced AI to predict mechanical failures in satellite ground stations before they happen, marking a significant shift from reactive repairs to proactive maintenance. Long Short-Term Memory networks can now forecast servo motor performance with remarkable precision, potentially preventing costly downtime in unmanned facilities where human oversight is minimal. Key Takeaways LSTM networks dramatically improve performance prediction accuracy for antenna control servo systems by learning complex patterns from operational data over time. This predictive capability enables proactive maintenance scheduling, reducing unscheduled downtime and improving reliability of critical systems like unmanned ground stations. By analyzing servo motor telemetry, LSTMs can detect equipment degradation early, optimizing maintenance schedules and extending asset lifespan.…