ECLSS Predictive Maintenance System
AI-powered anomaly detection and predictive maintenance for International Space Station life support systems — keeping crew safe before failures happen.
As human spaceflight ventures farther from Earth, reactive maintenance is a liability. A.U.R.A. transforms ECLSS from reactive to proactive — predicting failures before they endanger crew.
A.U.R.A. is a full-stack web application built on FastAPI with live WebSocket data streaming. Sensor readings from 7 ISS modules update every second across 20 parameters — accessible from any browser, no installation required.
Four machine learning models work in concert: Isolation Forest detects anomalies, Random Forest classifies fault type, LSTM predicts remaining useful life, and a Deep Q-Network recommends corrective actions.
Eight integrated views give operators complete situational awareness
Each model passes its output downstream — raw sensor noise to actionable recommendation in under a second
Random Forest classifies each anomaly into one of eight mission-critical fault categories with confidence scoring
Every screen built for clarity under pressure
Live 3D ISS model in Three.js with color-coded module health indicators and interactive orbit controls.
All locations with live sensor bars, fault banners, and DQN + LSTM status footers.
Historical Chart.js line charts with anomaly-flagged points, nominal band overlays, and data table.
Mann-Kendall trend analysis with severity-coded cards showing τ, p-value, slope, and recommendations.
Two-tier alert log with severity badges, confidence scores, timestamps, and per-alert acknowledgement.
LLM chat with sidebar showing live anomalies — synthesizes all four model outputs into plain-English analysis.
Explore the full feature breakdown or see rigorous testing validation for every system component.