Machine learning-based Remaining Useful Life (RUL) prediction for jet engines using the NASA CMAPSS dataset with an interactive Streamlit application.

Jet Engine Health Monitoring System using ML for Predictive Maintenance — a university group project.

End-to-end predictive maintenance: XGBoost RUL (RMSE 16.7 cycles, NASA C-MAPSS) + FastAPI + Streamlit + LangGraph agent on Google Cloud Run.

Predictive Maintenance Scheduling for Turbofan Engines — RUL Prediction + Resource-Constrained Metaheuristic Scheduling on NASA C-MAPSS (Purdue team project)

LSTM-based Remaining Useful Life prediction for turbofan engines using NASA CMAPSS dataset

AI-powered Aircraft Engine Predictive Maintenance System using NASA CMAPSS data, Machine Learning, and Streamlit for Remaining Useful Life (RUL) prediction.

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)

Predictive maintenance for turbofan engines - RUL prediction on NASA CMAPSS using Random Forest, XGBoost, sklearn Pipelines & MLflow

Real-time rocket telemetry anomaly detection — Isolation Forest + Autoencoder ensemble, 95% accuracy. Built for ISRO PSLV PS3 stage failure prevention.

✈️ 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.

Hybrid CNN-LSTM for Remaining Useful Life prediction on NASA C-MAPSS · RMSE 37.74 cycles · TensorFlow/Keras

End-to-end predictive maintenance system using NASA CMAPSS dataset with XGBoost, Streamlit dashboard, and Docker deployment.

HPC-optimized RUL prediction on NASA C-MAPSS FD001 dataset using XGBoost

End-to-end ML platform for turbofan engine RUL forecasting, failure classification, and anomaly detection using NASA CMAPSS FD001 dataset

High-performance ETL pipeline for predictive maintenance using NASA CMAPSS data (Vectorized/Clean Code)

NASA C-MAPSS 터보팬 엔진 LSTM 기반 잔존수명(RUL) 예측 | Phase 3 예지보전 프로젝트

Transformer-based remaining-useful-life (RUL) prediction for turbofan engines on NASA C-MAPSS — pure PyTorch, benchmarked against an LSTM baseline

Predictive maintenance platform with SHAP explainability, KS drift detection, OEE benchmarking, and interactive what-if scenarios. NASA C-MAPSS benchmark recast as mining ops, deployed on Streamlit Cloud.

Predictive maintenance and remaining useful life forecasting using stacked GRU autoencoders, temporal attention, and NASA C-MAPSS turbofan sensor data.