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

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)

✈️ 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

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

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

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

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

Repair-Aware Survival Analysis: Multi-domain maintenance optimization with NASA CMAPSS & SECOM validation.

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

Predictive maintenance of aircraft engines using NASA CMAPSS data with Random Forest, XGBoost, SHAP explainability, and Remaining Useful Life (RUL) prediction.

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.

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

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