turbofan-engine

Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

collection of predictive maintenance solutions for NASAs turbofan (CMAPSS) dataset

N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)

List of different up-to-date (2021) resources about Javascript engines

Remaining Useful Life estimation and sensor data generation by VAE and diffusion model on C-MAPSS dataset.

False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics

Gas Turbine / Engine Deck in Python for use in flight test.

A Digital Twin prototype for aircraft engine health management in order to identify possible faults and to predict its remaining useful life

Multi-Objective Optimization of ELM for RUL Prediction

Feature clustering and XIA for RUL estimation

Predicting RUL of turbofan of aircraft Engine using Regression techniques

The project aims to study the parametrical parameters of a two-spool turbofan with convergent nozzles

An interactive dashboard for analyzing the NASA Turbofan Engine dataset to detect anomalies and predict remaining useful life (RUL).

Production-grade predictive maintenance system using NASA C-MAPSS dataset. Real-time anomaly detection + RUL prediction with Kafka, LSTM, TimescaleDB, and MLflow.

Repositorio de trabajo de memoria de titulación: Pronóstico de fallas para mantenimiento predictivo usando metodologías de aprendizaje profundo supervisado.

This project builds a machine learning framework for predictive maintenance of turbofan engines, estimating Remaining Useful Life (RUL) from the NASA C-MAPSS sensor dataset. Methods included anomaly detection with CUSUM and autoencoders, and LSTM models, achieving significant RMSE reduction over baselines.

A MATLAB-based parametric cycle analysis and optimization tool for High-Bypass Turbofan Engines using constrained nonlinear programming (fmincon).

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

Conceptual design and preliminary thermodynamic analysis of a two-spool high-bypass turbofan engine developed using Autodesk Fusion 360 and GNU Octave.