Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.06031 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929380249305088 |
|---|---|
| author | Cheng, Zuyu Zhao, Zhengcai Wang, Yixiao Guo, Wentao Wang, Yufei Gao, Xiang |
| author_facet | Cheng, Zuyu Zhao, Zhengcai Wang, Yixiao Guo, Wentao Wang, Yufei Gao, Xiang |
| contents | This study presents a novel fault diagnosis model for urban rail transit systems based on Wavelet Transform Residual Neural Network (WT-ResNet). The model integrates the advantages of wavelet transform for feature extraction and ResNet for pattern recognition, offering enhanced diagnostic accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed model in identifying faults in urban rail trains, paving the way for improved maintenance strategies and reduced downtime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06031 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A WT-ResNet based fault diagnosis model for the urban rail train transmission system Cheng, Zuyu Zhao, Zhengcai Wang, Yixiao Guo, Wentao Wang, Yufei Gao, Xiang Information Retrieval This study presents a novel fault diagnosis model for urban rail transit systems based on Wavelet Transform Residual Neural Network (WT-ResNet). The model integrates the advantages of wavelet transform for feature extraction and ResNet for pattern recognition, offering enhanced diagnostic accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed model in identifying faults in urban rail trains, paving the way for improved maintenance strategies and reduced downtime. |
| title | A WT-ResNet based fault diagnosis model for the urban rail train transmission system |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2406.06031 |