Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

remaining Useful Life (RUL) Prediction of Mechanical Bearings using Continuous Wavelet Transform (CWT), Convolution Neural Network (CNN), and Long Short Term Memory (LSTM) unit

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)

A Framework for Remaining Useful Life Prediction Based on Self-Attention and Physics-Informed Neural Networks

The source code of paper: Trend attention fully convolutional network for remaining useful life estimation in the turbofan engine PHM of CMAPSS dataset. Signal selection, Attention mechanism, and Interpretability of deep learning are explored.

Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model - Implementation of Research Paper : https://doi.org/10.1016/j.isatra.2019.08.058

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

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

A collection of datasets for RUL estimation as Lightning Data Modules.

CeRULEo: Comprehensive utilitiEs for Remaining Useful Life Estimation methOds

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

Remaining Useful Life (RUL) prediction for Turbofan Engines

Multi-Objective Optimization of ELM for RUL Prediction

An artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of Lithium Ion batteries subject to condition monitoring. The ANN model takes the capacity attribute as a target against multiple measurement values as the inputs, and the life expectancy as the output.

Deep learning of lithium-ion battery SOH using the DeTransformer model learns the aging characteristics of the battery and then makes predictions about the battery SOH in order to monitor the health of batteries in electric vehicles.

Remaining useful life prediction. Degradation path approximation (DPA) is a highly easy-to-understand and brand-new solution way for data-driven RUL prediction. Many research directions on DPA can be further studied.

predictive-maintenance-fault-classification(CWRU data)-and-remaining-useful-life(NASA’s Turbofan Engine )

Feature clustering and XIA for RUL estimation

An encoder-transformer architecture-based framework for multi-variate time series prediction with a prognostics use case.