rul-prediction

This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.

In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.

RUL prediction for C-MAPSS dataset, reproduction of this paper: https://personal.ntu.edu.sg/xlli/publication/RULAtt.pdf

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

Unified PHM framework for Remaining Useful Life (RUL) prediction, fault diagnosis, fault detection, and anomaly detection for bearings, turbofan engines, and other industrial systems.

This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

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

Remaining Useful Life (RUL) prediction for Turbofan Engines

Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, condition-based maintenance, CBM, predictive maintenance, PdM, prognostics health management, PHM

An awesome list of papers on remaining useful life (RUL) prediction from arXiv

Recurrent Plot on Multivariate Time Series Windows for Transfer Learning–based RUL estimation

Multi-Objective Optimization of ELM for RUL Prediction

Prediction of Remaining Useful Life (RUL) of NASA Turbofan Jet Engine using libraries such as Numpy, Matplotlib and Pandas. Prediction is done by training a model using Keras (TensorFlow).

Feature clustering and XIA for RUL estimation

AeroBMS: A Physics-Informed Digital Twin & AI Predictive Maintenance dashboard for NASA Lithium-Ion batteries using XGBoost & Streamlit.

Industrial Anomaly Detection & Predictive Analytics Library

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

Advanced Condition Monitoring and Remaining Useful Life Prediction Framework using Deep Learning for Industrial Equipment Prognosis and Predictive Maintenance