Design of Faster R-CNN-Based Fault Detection Method for Subway Vehicles

Abstract

A substantial amount of maintenance and fault data is not properly utilized in the daily maintenance of pantographs in urban metro cars. Pantograph fault analysis can begin with three factors: the external environment, internal flaws, and joint behavior. Based on the analysis of pantograph fault types, corresponding measures are proposed in terms of pantograph fault handling and maintenance strategies, in order to provide safety guarantee for the safe and effective realization of rail transit vehicle speed-up and also provide reference for the maintenance and overhaul of pantographs. For the problem of planned maintenance no longer meeting current pantograph maintenance requirements, a defect diagnosis system based on a combination of faster R-CNN neural networks is presented. The pantograph image features are extracted by introducing an alternative to the original feature extraction module that can extract deep-level image features and achieve feature reuse, and the data transformation operations such as image rotation and enhancement are used to expand the sample set in the experiment to enhance the detection effect. The simulation results demonstrate that the diagnosis procedure is quick and accurate.