nasa-cmapss

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

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

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

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

End-to-end predictive maintenance for NASA C-MAPSS turbofan engines: XGBoost RUL forecasting, calibrated uncertainty, explainable AI, React, FastAPI, and Docker—fully local.

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

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

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)

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

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

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

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.

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

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

Unsupervised anomaly detection on NASA C-MAPSS turbofan sensor data, using reconstruction-error-based failure detection. Currently includes a data pipeline with RUL labeling and a PCA-based statistical baseline (SPE + Hotelling's T²); LSTM/Transformer autoencoders in progress.

Production-ready turbofan predictive maintenance platform using time-series analysis and deep learning to forecast NASA CMAPSS engine remaining useful life (RUL), with feature engineering, model evaluation, FastAPI APIs, and interactive monitoring dashboards.

Predição de vida útil restante (RUL) de motores turbofan com o dataset NASA C-MAPSS

Machine learning project using NASA CMAPSS data to predict aircraft engine Remaining Useful Life.