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

End-to-end predictive maintenance system: XGBoost RUL prediction (RMSE 16.7 cycles on NASA CMAPSS) + FastAPI + Streamlit + LangGraph agentic AI, deployed on Google Cloud Run.

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

✈️ 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.

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

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

Predicts remaining useful life of turbofan engines from NASA CMAPSS sensor data (Gradient Boosting, RMSE ≈ 18.87); model exported as deployment-ready artifacts for a real-time inference API.

End-to-end predictive maintenance ML system — RUL prediction, anomaly detection, FastAPI, Docker

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.

Aircraft engine failure prediction · CatBoost · FastAPI + Docker · NASA CMAPSS · ROC-AUC 0.991

Predictive maintenance for aircraft turbofan engines, RUL prediction using ML & LSTM on NASA CMAPSS dataset

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

Production-grade AI Predictive Maintenance Copilot using NASA CMAPSS data, combining classical ML, deep learning (LSTM/GRU), unified inference, FastAPI, Streamlit, GenAI (RAG), Docker, and Google Cloud Run deployment.

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

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

Official code for arXiv:2604.13459 - Asymmetric-Loss CNN-BiLSTM-Attention for Industrial RUL Prediction