School of Power Technology, Liuzhou Railway Vocational Technical College, Liuzhou Guangxi 545616, China.
School of Automatic Control, Liuzhou Railway Vocational Technical College, Liuzhou Guangxi 545616, China.
Comput Math Methods Med. 2022 Jul 8;2022:1400658. doi: 10.1155/2022/1400658. eCollection 2022.
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.
在城市地铁车辆受电弓的日常维护中，大量的维护和故障数据并没有得到妥善利用。受电弓故障分析可以从三个因素入手：外部环境、内部缺陷和连接行为。基于对受电弓故障类型的分析，提出了相应的措施，针对受电弓的故障处理和维护策略，为轨道交通车辆提速的安全有效实现提供安全保障，也为受电弓的维护和检修提供参考。针对计划性维护不再满足当前受电弓维护要求的问题，提出了一种基于更快的 R-CNN 神经网络组合的缺陷诊断系统。通过引入一种替代原始特征提取模块的方法来提取深层图像特征并实现特征重用，提取受电弓图像特征，并通过图像旋转和增强等数据变换操作来扩展实验中的样本集，以增强检测效果。仿真结果表明，该诊断过程快速准确。
