A WT-ResNet based fault diagnosis model for the urban rail train transmission system

TL;DR

This paper introduces a WT-ResNet model combining wavelet transform and residual neural networks to improve fault diagnosis accuracy and robustness in urban rail train transmission systems, enhancing maintenance and reducing downtime.

Contribution

The novel integration of wavelet transform with ResNet for fault diagnosis in urban rail systems offers improved accuracy and robustness over existing methods.

Findings

Effective fault identification in urban rail trains

Enhanced diagnostic accuracy and robustness

Potential for improved maintenance strategies

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.