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A WT-ResNet based fault diagnosis model for the urban rail train transmission system
June 10, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Zuyu Cheng, Zhengcai Zhao, Yixiao Wang, Wentao Guo, Yufei Wang, Xiang Gao
arXiv ID
2406.06031
Category
cs.IR: Information Retrieval
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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