Comparison of various transfer learning models with the hybridization of am FCNN for battery remaining-useful-life (RUL) prediction.
This repository was made for the paper: 'Image- and health indicator-based transfer learning hybridization for battery RUL prediction' If you make use of this code for your own research, please cite: (J. Couture and X. Lin, "Image- and Health Indicator-Based Transfer Learning Hybridization fro Battery RUL Prediction" in Engineering Applications of Artificial Intelligence, https://doi.org/10.1016/j.engappai.2022.105120) Author: Jonathan Couture, OntarioTech University E-mail: J.Couture17@hotmail.com Date: November 17th 2022
This study seeks to quantify the advantage of using images alongside health indicators with the use of readily available transfer learning models. This article uses the Toyota/MIT battery dataset to create the image and health indicator dataset. If you'd like to try out the network with the data, you'd need to download the Toyota/MIT battery dataset from the source at: https://data.matr.io/1/projects/5c48dd2bc625d700019f3204 and run the Matlab scripts