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

锂电池数据集 CALCE

Analysis for NASA data sets

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.

Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

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

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

A project focused on the improvement for remaining useful life estimation.

Unofficial PyTorch implementation of the paper "Variational encoding approach for interpretable assessment of remaining useful life estimation"

Algorithms for Battery Mangerment System

Awesome Deep Fault Diagnosis

ASE2306-Capstone Project [2019/20 T3] - Aircraft Engine Lifetime Prediction with Machine Learning

Multimodal Isotropic Neural Architecture with Patch Embedding to both time series and image data for classification purposes.

Expandable Isotropic Multimodal Patch Learning Neural Architecture for the Nano-modal (9) time-series and images data.

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).

SIREN Scalable, Isotropic Recursive Column Multimodal Neural Architecture with Device State Recognition Use-Case