Identification Method for Missing Cotter Pins in Catenary Systems Based on Stacked Denoising Autoencoder Network

Abstract

Objective When using convolutional neural networks (CNNs) for the state detection of catenary system cotter pins, the imbalance between positive and negative samples leads to a low detection rate of missing pins in the network model. Thus, a single-stage detection network is employed for multi-level precise localization of cotter pins, the state features of the cotter pins are reconstructed in combination with SDAE (stacked denoising autoencoder), thereby achieving efficient detection of missing cotter pins. Method First, a single-stage localization detection network is employed for the regression of cotter pin positions, and the localization results are used as input for SDAE. Different levels of depth noise are introduced into various structural layers of the denoising autoencoder network. By minimizing reconstruction errors, the method enhances semantic understanding of the cotter pin localized images, allowing for accurate assessment of their condition. Additionally, due to the constraints on the size of the localized images, the computational load of the SDAE is relatively low, same with the network time complexity. Result & Conclusion Extensive experimental results demonstrate that the SDAE, based on YOLO v5 algorithm, can accurately detect missing cotter pins at various locations within the catenary system.

Keywords

• metro
• catenary
• cotter pin
• yolo v5 algorithm
• stacked denoising autoencoder