P8 · AEGIS · static

🛩️ Turbofan Pre-emptive-Maintenance Simulator

Project 8 · NASA C-MAPSS. Pick an engine and rewind or advance how much of its life you've observed — the champion RUL model re-predicts from that engine's real sensor history at every cycle, so you watch it age. Its job: schedule maintenance before failure, not after.

Fleet (sub-dataset)

Champion model:

🖥️ Display

🎚️ Input Stimulus

Move these — the display responds live.

1 · Which engine 1 (across the fleet)

2 · Life observed 0 flight-cycles

Fleet maintenance queue — prioritized by urgency

The whole fleet at each engine's last observed cycle (the real NASA test point) — the crew's actual work list, independent of the sliders above.

Show all engines (including routine)

Engine	Priority	Time to Failure	Predicted RUL	AI-Predicted Failure @ Cycle	Last Observed Cycle

Static edition — runs entirely in your browser, no server. FD001/FD002/FD003 predictions are bit-exact reproductions of the deployed champion pipelines (RMSE identical to the benchmark to 10+ decimals). FD004 (CatBoost) is anchored to the deployed model's exact per-engine predictions; the current RUL and fleet queue match the live app exactly, and the historical rewind uses a faithful re-fit anchored to those values. A decision-support demo on the open C-MAPSS dataset — not a certified maintenance system.