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

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

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

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

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.

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

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

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

End-to-end Industrial AI system using Machine Learning and SQL to predict machinery failure. Achieved $88.2M in simulated savings with an 0.81 R² score and a real-time Streamlit dashboard.

GEBiLSTM-Attn: Novel Deep Learning Architecture for RUL Prediction (NASA CMAPSS)

TCN ensemble for turbofan engine RUL forecasting on NASA C-MAPSS - RMSE ≤12.8 cycles, SHAP explainability, FastAPI + Next.js dashboard, Docker-ready

AeroSafe: NASA C-MAPSS Aircraft Engine RUL Prediction & Predictive Maintenance Dashboard.

Predictive maintenance system classifying NASA turbofan engine health into actionable risk categories (Good/Moderate/Warning) using CatBoost and sensor data.