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DL-based-Intelligent-Diagnosis-Benchmark

Source codes for the paper "Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study"

Model DevelopmentClassical MLPython
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创建于 2019-12-28 · 更新于 2026-10-07 · 今日第 13915 名
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ZhaoZhibin

ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark 排名第 13,915，有 778 个星标。

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ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark 排名第 13,915，此处有 8 个同类条目；bnu-wangxun/Deep_Metric 排名第 13,918。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Classical ML
关键词Benchmarks
星标数778
分叉数186
贡献者1
创建日期2019-12-28
更新日期2026-10-07
1 天星标数0
7 天星标数0
排名13,915
上次排名13,798
排名变化-117
7 天星标排名14,185
上次 7 天星标排名14,204
7 天星标排名变化19
总星标排名13,648
上次总星标排名13,633
总星标排名变化-15
国家China
README 摘录

DL-based-Intelligent-Diagnosis-Benchmark Code release for Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study **ISA Transactions** **arXiv** by Zhibin Zhao, Tianfu Li, and Jingyao Wu. 14, we modified the errors of the dataloaders related to XJTU dataset. Guide This project just provides the baseline (lower bound) accuracies and a unified intelligent fault diagnosis library which retains an extended interface for everyone to load their own datasets and models by themselves to carry out

new studies. Meanwhile, all the experiments are executed under Window 10 and Pytorch 1. 1 through running on a computer with an Intel Core i7-9700K, GeForce RTX 2080Ti, and 16G RAM. R_A: random split with data augmentation, R_NA: random split without data augmentation, O_A: order split with data augmentation Requirements Python 3. 40 Datasets **CWRU Bearing Dataset** **MFPT Bearing Dataset** **PU Bearing Dataset** **UoC Gear Fault Dataset** **XJTU-SY Bearing Dataset** **SEU Gearbox Dataset** **JNU Bearing

Dataset** Pakages This repository is organized as: AE_Datasets contains the loader of different datasets for AE models. CNN_Datasets contains the loader of different datasets for MLP, CNN, and RNN models. datasets contains the data augmentation methods and the Pytorch datasets for 1D and 2D signals. models contains the models used in this project. utils contains the functions for realization of the training procedure. py to test MLP, CNN, and MLP models for example, use the following commands to

test MLP for SEU with mean-std normalization and data augmentation use the train_ae. py to test AE models for example, use the following commands to test SAE for SEU with mean-std normalization and data augmentation Citation Codes: Paper: Contact zhibinzhao1993@gmail.

常见问题

记录了多少星标和分叉？

ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark 有 778 个星标和 186 个分叉。

仓库排名是多少？

ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark 排名第 13,915。

记录的语言和类别是什么？

ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark 有 778 个星标，语言为 Python，类别为 Model Development。

哪个仓库排名最接近？

ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark 与 bnu-wangxun/Deep_Metric 比较，后者排名第 13,918，有 778 个星标。

README

DL-based-Intelligent-Diagnosis-Benchmark

Code release for Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study ISA Transactions arXiv by Zhibin Zhao, Tianfu Li, and Jingyao Wu.

Correction

  • 2020.05.14, we modified the errors of the dataloaders related to XJTU dataset.

Guide

This project just provides the baseline (lower bound) accuracies and a unified intelligent fault diagnosis library which retains an extended interface for everyone to load their own datasets and models by themselves to carry out new studies. Meanwhile, all the experiments are executed under Window 10 and Pytorch 1.1 through running on a computer with an Intel Core i7-9700K, GeForce RTX 2080Ti, and 16G RAM.

R_A: random split with data augmentation, R_NA: random split without data augmentation, O_A: order split with data augmentation

Requirements

  • Python 3.7
  • Numpy 1.16.2
  • Pandas 0.24.2
  • Pickle
  • tqdm 4.31.1
  • sklearn 0.21.3
  • Scipy 1.2.1
  • opencv-python 4.1.0.25
  • PyWavelets 1.0.2
  • pytorch >= 1.1
  • torchvision >= 0.40

Datasets

Pakages

This repository is organized as:

  • AE_Datasets contains the loader of different datasets for AE models.
  • CNN_Datasets contains the loader of different datasets for MLP, CNN, and RNN models.
  • datasets contains the data augmentation methods and the Pytorch datasets for 1D and 2D signals.
  • models contains the models used in this project.
  • utils contains the functions for realization of the training procedure.

Usage

  • download datasets

  • use the train.py to test MLP, CNN, and MLP models

  • for example, use the following commands to test MLP for SEU with mean-std normalization and data augmentation

  • python train.py --model_name MLP --data_name SEU --data_dir ./Data/Mechanical-datasets --normlizetype mean-std --processing_type O_A --checkpoint_dir ./Benchmark/Benchmark_Results/Order_split/SEU/MLP_mean-std_augmentation

  • use the train_ae.py to test AE models

  • for example, use the following commands to test SAE for SEU with mean-std normalization and data augmentation

  • python train_ae.py --model_name Sae1d --data_name SEU --data_dir ./Data/Mechanical-datasets --normlizetype mean-std --processing_type O_A --checkpoint_dir ./Benchmark/Benchmark_Results/Order_split/SEU/Sae1d_mean-std_augmentation

Citation

Codes:

@misc{Zhao2020,
author = {Zhibin Zhao and Tianfu Li and Jingyao Wu and Chuang Sun and Shibin Wang and Ruqiang Yan and Xuefeng Chen},
title = {Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark}},
}

Paper:

@article{zhao2020deep,
  title={Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study},
  author={Zhibin Zhao and Tianfu Li and Jingyao Wu and Chuang Sun and Shibin Wang and Ruqiang Yan and Xuefeng Chen},
  journal={ISA Transactions},
  year={2020}
}

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