Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction

锂电池数据集 CALCE

Analysis for NASA data sets

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

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

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

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

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

Algorithms for Battery Mangerment System

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

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

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).

This project is for classifying the Remaining Useful Life (RUL) Time of bearing

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