There are 2 repositories under rul topic.
Deep learning in PHM,Deep learning in fault diagnosis,Deep learning in remaining useful life prediction
Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs
The project focused on "Battery Remaining Useful Life (RUL) Prediction using a Data-Driven Approach with a Hybrid Deep Model combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM)." This repository aims to revolutionize battery health estimation by leveraging the power of deep learning to predict the remaining useful life
Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.
Remaining useful life estimation of NASA turbofan jet engines using data driven approaches which include regression models, LSTM neural networks and hybrid model which is combination of VAR with LSTM
Unofficial PyTorch implementation of the paper "Variational encoding approach for interpretable assessment of remaining useful life estimation"
ASE2306-Capstone Project [2019/20 T3] - Aircraft Engine Lifetime Prediction with Machine Learning
Awesome Deep Fault Diagnosis
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).
Server simulation for monitoring remaining useful life of turbofan jet engines