MPNet

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# MPNet: A lightweight fault diagnosis network for rotating machinery 
Aiming at the real-time accurate detection of typical rotating machinery faults in the practical strong noise industrial scenarios, we propose a sound signal-based rotating machinery fault diagnosis method, and designs a novel lightweight fault diagnosis network called MPNet, which well balances accuracy, real-time performance and model size.
Detailed data will be available soon

## Proposed method
![image](https://github.com/xgli411/MPNet/blob/main/image/Implementation%20method.png)

## Network srchitecture
![image](https://github.com/xgli411/MPNet/blob/main/image/MPNet.png)

## Visualization of experimental results
### (1) Anti-noise experiment on the DCASE 2023 Bearing dataset
![image](https://github.com/xgli411/MPNet/blob/main/image/Anti-noise%20experiment.png)
### (2) Experiment on the self-made dataset
#### a. Diagnostic performance comparison
![image](https://github.com/xgli411/MPNet/blob/main/image/result%20on%20the%20self-made%20dataset.png)
#### b. Feature distribution visualization
![image](https://github.com/xgli411/MPNet/blob/main/image/tsne.png)

## Cite this article

🔗🔗Paper link：(https://www.sciencedirect.com/science/article/abs/pii/S0263224124013836)

@article{LIU2025115498,
title = {MPNet: A lightweight fault diagnosis network for rotating machinery},
journal = {Measurement},
volume = {239},
pages = {115498},
year = {2025},
issn = {0263-2241},
doi = {https://doi.org/10.1016/j.measurement.2024.115498},
url = {https://www.sciencedirect.com/science/article/pii/S0263224124013836},
author = {Yi Liu and Ying Chen and Xianguo Li and Xinyi Zhou and Dongdong Wu}
}

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