rul-estimation

IEEE Published | ML model for Aircraft Engine RUL prediction using XGBoost & Random Forest on NASA C-MAPSS dataset. RMSE: 23.8, R²: 0.67. Flask web app + PostgreSQL. ICMCSI 2025 (Paper ID: ICMCSI-472)

A predictive maintenance model for estimating machine remaining useful life (RUL) with uncertainty. Instead of predicting a single value, the model outputs a range of plausible outcomes, enabling more informed maintenance decisions.

✈️ NASA Predictive Maintenance System >> 🚀 End-to-end predictive maintenance system for turbofan engines — detects failure 122 cycles early using LSTM, PCA, and explainable AI (XAI), deployed as a production-grade Streamlit dashboard.

🚀 Predictive maintenance for NASA turbofan engines — RUL estimation using ML & deep learning

XGBoost wear state classifier + RUL regressor for CNC milling tool fleets. 96.8% accuracy, ~18-cut MAE. Streamlit fleet dashboard.

Production-grade predictive maintenance engine for industrial fleets with remaining useful life (RUL) estimation and cost-optimized scheduling.