Space
Maintenance
Machine Learning

Securing critical launch infrastructure through predictive maintenance

Machine learning-based predictive maintenance to reduce unplanned downtime and increase the reliability of space infrastructure.

CLIENT
CNES
INDUSTRIE
Space
METIER
Maintenance
TECHNOLOGIE
Machine Learning

Contexte

The launch program Ariane 6 integrates several hundred critical cryogenic valves that generate a massive stream of telemetry data. Even the slightest performance deviations create major operational risks and high costs. Predicting these anomalies is vital to optimizing launch timelines and ensuring absolute reliability in extreme environments.

Solution

We have deployed a Predictive Maintenance system using machine learning to analyze valve response times. By correlating pressures, temperatures, and historical data, the algorithm identifies any deviation from nominal behavior, allowing for early failure detection without interrupting ground operations.

-50%
of unplanned downtime
-20%
maintenance costs
+25%
equipment lifespan

Bénéfices

  • Reduction in launch delays caused by ground equipment failures
  • Optimization of operational efficiency and securing launch windows
  • Profitability by preventing service interruptions
Caroline
Lead Data Scientist

"The major challenge was synchronizing heterogeneous sources in a critical environment. We overcame this by developing robust temporal models that isolate weak signals despite ambient noise. This precision now ensures proactive planning for the management of these highly strategic infrastructures."

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