基于BP神经网络的铁路客运设备故障监测模型设计与研究

由于铁路客运设备种类众多并且分布区域分散，致使人工巡检工作效率低。为了提高巡检工作的效率，实现铁路客运设备巡检工作智能化，本文提出并设计了基于BP神经网络的铁路客运设备故障监测模型。通过无线传感器获取影响客运设备状态优劣的因素，运用所建模型进行决策，判断铁路客运设备是否运行正常并能准确地诊断出故障部位，实现了铁路客运设备巡检工作智能化的目标。最后通过对机房空调设备的仿真研究，验证了所建模型的有效性。

Fault monitoring model for railway passenger transport equipment based on BP neural network

Due to many kinds of railway passenger transport equipments and scattered distribution area, the work efficiency of manual inspection was low. In order to improve the efficiency of the inspection working and implement the intelligent inspection work for the equipments, this article proposed and designed a fault monitoring model for the equipments based on BP neural network. The equipment status and influencing factors were gained by wireless sensors. The model was used to determine whether the equipment worked normally, accurately diagnose the fault parts. The goal of intelligent inspection work was implemented. Finally, the air conditioning equipment was simulated to verify the validity of the model.