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)

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

Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

Remaining Useful Life (RUL) prediction for Turbofan Engines

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

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

Predict the Remaining Useful Life (RUL) of aircraft engines using NASA's Turbofan Engine Degradation Simulation Data. Leverage machine learning for predictive maintenance to reduce costs and prevent failures.