This is the repository for the benchmark study article Impact of Deep Learning Optimizers and Hyperparameter Tuning on the Performance of Bearing Fault Diagnosis.
We implemented end-to-end optimization benchmark code using public bearing fault datasets and state-of-the-art fault diagnosis models. This code provides public dataset download, data preprocessing, quasi-random hyperparameter sampling, and model training.
To use this code, we recommended to install libraries on the anaconda virtual environment. Required libraries will be installed following instructions below.
conda create -n {your virtual env name} python=3.10.6
conda activate {your virtual env name}
pip install --upgrade pip
pip install -r requirements.txt
Note: We tested this code in PC using Ubuntu Linux and CUDA GPU. Experimental specifications are listed below.
| Type | Specification |
|---|---|
| OS | Ubuntu 18.04 |
| CPU | Intel Core i9-10900K @ 3.70 GHz |
| RAM | 128 GB |
| GPU | NVIDIA GeForce RTX 2080 SUPER x2 |
| CUDA version | 11.2 |
| CUDNN version | 7.6.5 |
We provide short demo code. Check tutorial.ipynb.
MIT License.
If this code is helpful, please cite our paper Link:
@ARTICLE{10141610,
author={Lee, Seongjae and Kim, Taehyoun},
journal={IEEE Access},
title={Impact of Deep Learning Optimizers and Hyperparameter Tuning on the Performance of Bearing Fault Diagnosis},
year={2023},
volume={11},
number={},
pages={55046-55070},
doi={10.1109/ACCESS.2023.3281910}}