Requested by NASA MSFC-HP40
Implementing AI and Machine Learning (ML) for predictive maintenance in off-planet Environmental Control and Life Support Integrated Systems (ECLIS) dramatically improves crew safety and operational efficiency, reducing both maintenance preparation time and safety incident rates by over 45%.
As human spaceflight ventures farther from Earth, the reliability and autonomy of life support systems become mission-critical. An AI/ML-powered Predictive Maintenance System—leveraging the Major Constituent Analyzer (MCA), Monitoring and Control System (MCS), and a network of advanced sensors—transforms ECLIS from a reactive to a proactive safety net. This system continuously monitors, analyzes, and forecasts the health of all vital subsystems, enabling early detection of anomalies and optimized maintenance scheduling.
The A.U.R.A. system processes real-time data from 28+ sensors across 13 critical subsystems including atmosphere revitalization, oxygen generation, water recovery, temperature/humidity control, and more. Advanced anomaly detection algorithms identify subtle deviations from normal operation, while machine learning models predict component failures before they occur.
AI/ML algorithms process continuous data streams from the MCA, MCS, and other environmental sensors to identify subtle deviations from normal operation. Early anomaly detection allows for intervention before faults escalate, minimizing unplanned downtime and risk to crew safety.
A.U.R.A. monitors 13+ critical life support subsystems including Atmosphere Revitalization, Oxygen Generation, Water Recovery, Temperature/Humidity Control, Trace Contaminant Control, and more. Real-time visualization of all parameters ensures complete system awareness.
Predictive analytics estimate the remaining useful life of components, recommending maintenance only when necessary. Reduced spare part requirements and fewer unnecessary interventions lower mission costs and conserve mass/volume—critical for off-planet operations.
An interactive digital twin of the spacecraft enables operators to visualize system states, test maintenance procedures, and validate repairs before implementing them in actual hardware. Reinforcement learning models trained on the digital twin improve decision-making accuracy.
Isolation Forest algorithms identify outliers and irregular patterns in sensor readings, detecting potential system faults and sensor drift. This data preprocessing ensures training datasets are accurate and representative, improving overall model reliability.
Automated diagnostics and predictive alerts streamline astronaut workflows, reducing cognitive load and preparation time. Integrated health monitoring links ECLIS performance with crew health analytics, providing a holistic approach to risk management.
Comprehensive user management system with role-based permissions (admin, operator, analyst) ensures appropriate access to sensitive system data and configuration options.
AI-guided troubleshooting and maintenance checklists cut average preparation time per mission by over 60%. Safety incident rates are reduced by nearly half, ensuring system uptime and mission continuity.
A.U.R.A. provides comprehensive monitoring of the following life support subsystems:
