AI Preventive Maintenance System

A.U.R.A. - Automated User Resource Analyzer

Executive Summary

Implementing AI and Machine Learning (ML) for predictive maintenance in off-planet Environmental Control and Life Support Systems (ECLSS) dramatically improves crew safety and operational efficiency, reducing both maintenance preparation time and safety incident rates by over 45%.

System Overview

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 transforms ECLSS from reactive to proactive safety management, 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, and temperature/humidity control. Advanced anomaly detection algorithms identify subtle deviations from normal operation, while machine learning models predict component failures before they occur.

Critical Subsystems Monitored

A.U.R.A. provides comprehensive monitoring of 13+ life support subsystems:

✓ Atmosphere Revitalization System - O₂ and CO₂ pressure, humidity levels
✓ Oxygen Generation System - O₂ output rate and purity
✓ Water Recovery System - Water purity (TOC) and production rate
✓ Temperature & Humidity Control - Cabin temperature and environmental conditions
✓ Trace Contaminant Control - NH₃, H₂, and other gaseous contaminants
✓ Waste Management System - Waste containment monitoring
✓ Fire Detection & Suppression - Fire hazard detection
✓ Air Circulation/Ventilation - Airflow rate and distribution
✓ Pressure Control - Cabin pressure stability
✓ Microbial Monitoring - Bacterial and fungal count
✓ Vacuum System - External pressure sensors
✓ Mass Spectrometer Module - Atmospheric composition analysis