OpenBFD is an open-source deep learning tool for bearing fault diagnosis built with PyTorch. It provides comprehensive solutions for detecting and classifying bearing faults using raw vibration data and advanced deep learning techniques.
- Deep learning models specifically designed for bearing fault detection
- Real-time vibration data analysis
- Support for custom dataset training
- Comprehensive visualization tools for fault analysis
- Education: Ms.D. in Computer Science, China University of Mining and Technology
- Key Skills: Software Engineering, Deep Learning, Machine Learning, Data Science, IoT, Predictive Maintenance.
Islomkhon Nizomkhonov was born in 1994 in Taskent, Uzbekistan. He is a senior software engineer with over 6 years of experience in the field of machine learning and predictive maintenance. He has worked with several industries and big companies like XCMG (徐工集团) and Uztelecom (Uzbekistan National Telecom Provider), providing solutions using deep learning techniques. His research primarily focuses on using neural networks for fault diagnosis in industrial IoT systems.
- Education: Associate Prof. in Artificial Intelligence and Cloud Computing, China University of Mining and Technology
- Key Skills: AI, Cloud Computing, Industrial Automation, Mechanical Equipment Condition Monitoring
Mr. ZhangBo is Islomkhon Nizomkhonov's master's degree supervisor for his research project on distributed deep neural networks, which Islomkhon conducted at China University of Mining and Technology.
Mr. ZhangBo, was born in 1981 in Xuzhou, Jiangsu Province in China. He graduated from the Institute of Computing Technology, Chinese Academy of Sciences, with a Ph.D., and completed his postdoctoral research at China University of Mining and Technology. In 2018, he was a visiting scholar at the Institute of Data Science (IDS), National University of Singapore (NUS). He is currently an associate professor at the School of Computer Science, China University of Mining and Technology.
ZhangBo is engaged in theoretical and applied research in artificial intelligence, machine learning, pattern recognition, mechanical equipment condition monitoring, and cloud computing. He has led one project funded by the Jiangsu Provincial Basic Research Program (Natural Science Foundation) under the Youth Fund, one project under the 62nd batch of the China Postdoctoral Science Foundation, and one project under the Jiangsu Postdoctoral Research Funding Program. In recent years, he has applied for/been granted 3 invention patents, published 7 papers in SCI journals, and 3 papers in EI journals. He serves as a reviewer for international journals such as IEEE Transactions on Industrial Electronics and Journal of Computer Science as well as other domestic and international journals.
In December 2016, the project titled "Research and Application of Belt Conveyor Monitoring and Fire Warning Technology" led by ZhangBo won the second prize of the "China Coal Industry Science and Technology Award" from the China National Coal Association and the China Coal Society. The project was successfully applied to the belt conveyors at the underground mines of the Zhongmei Pingshuo Group, effectively addressing the challenges of condition monitoring, fault diagnosis and forecasting, as well as fire warning for belt conveyors under operational conditions in the mining environment.
git clone https://github.com/islomkhon/OpenBFD.git
cd OpenBFD
pip install -e .The overall structure of the project is outlined below in the form of a project tree. Please review it carefully before starting. Each README.md file inside the subdirectories contains specific documentation related to the files and the purpose of that directory.
OpenBFD/
│
├── Datasets/
│ ├── DatasetName/
│ │ ├── RawData/
│ │ ├── preprocessor.py
│ │ └── README.md
│ │
│ └── README.md
│
├── DLModels/
│ ├── ModelInputOutputType/
│ │ └── ModelName/
│ │ ├── Model.py
│ │ ├── ResearchPaper.pdf
│ │ └── README.md
│ │
│ └── README.md
│
├── FitLoops/
│ ├── ModelInputOutputType/
│ │ └── train.py
│ │
│ └── README.md
│
├── Notebook/
│ ├── ModelInputOutputType/
│ │ └── ModelName/
│ │ └── DatasetName/
│ │ └── ExperimentName.ipynb
│ │
│ └── README.md
│
├── Pictures/
│ ├── Authors/
│ └── README.md
│
├── README.md
├── setup.py
└── 伊斯罗贡+基于云边协同的工程机械故障诊断系统研究+117.pdf
We welcome contributions! Please follow the project structure and the conventions provided in the README.md file inside the contributing subdirectories. Feel free to:
- Report bugs
- Suggest new features
- Submit pull requests
- Improve README.md documentations
If you use OpenBFD in your research, please cite:
@software{openbfd,
title = {OpenBFD: An Open-Source Tool for Bearing Fault Diagnosis},
author = {Islomkhon Nizomkhonov (小伊) , Mr. ZhangBo (张博)},
year = {2025},
url = {https://github.com/islomkhon/OpenBFD}
}This project is licensed under the MIT License - see the LICENSE file for details.
- Contributors and maintainers
- PyTorch community
- Open-source vibration analysis tools that inspired this project
- GitHub Issues: For bug reports and feature requests
- Email: islomkhon@mail.com
- Instagram: islomkhonnizomkhonov
If you find this project helpful, you can support OpenBFD by making donations via:
| Alipay | |
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