Fault diagnosis based on deep learning for current-carrying ring of catenary system in sustainable railway transportation

Abstract

In the intelligent traffic transportation, the security and stability are vital for the sustainable transportation and efficient logistics. The fault diagnosis on the catenary system is crucial for the railway transportation. For purpose of improving the detection capability for the faulted current-carrying ring and maintaining the efficiency of the railway system, a fault diagnosis method for the current-carrying ring based on an improved RetinaNet model with the spatial attention map and channel weight map is proposed. The local and global features are utilized respectively. The spatial attention maps are embedded into the original convolutional neural network to emphasize the interested local features and weaken the influence of other objects and background. The channel weight maps are introduced into the feature pyramid network of detection network to weight the multi-scale feature maps in channels. The representative global features are focused and unnecessary features are suppressed. The experimental results indicate that the proposed method has increased fault diagnosis accuracy for faulted current-carrying rings compared with the original detection network based on different backbones. It can be used in improving efficiency and safety of railway transport system.