AeroRUL – Turbofan Engine Remaining Useful Life Prediction
An end-to-end predictive-maintenance system for jet engines: raw sensor telemetry in, a maintenance recommendation out. Five genuinely different models — XGBoost, LSTM, TCN, Transformer, and Weibull AFT survival analysis — are compared consistently across NASA's four CMAPSS subsets using RMSE, MAE, and the asymmetric PHM08 scoring function, with split conformal prediction intervals for calibrated uncertainty. A real evaluation bug — scoring against the capped training label instead of true RUL, which understated XGBoost's FD002 RMSE by 45% (15.2 vs. the real 27.7) — is caught, explained, and fixed with before/after numbers across four files. Served by a FastAPI backend behind a React/TypeScript fleet-health dashboard that runs backend-free from static snapshots or against a live API, containerized with Docker, and walked through across five fully-executed Jupyter notebooks.
Proof points
- 5 Models × 4 Subsets Compared
- Split Conformal Intervals
- Caught & Fixed Eval Bug
Technologies
Python, XGBoost, PyTorch, FastAPI, React, TypeScript, MLflow, Docker, uv
Links