摘要
高速动车组在高速运行过程中会发生多种设备故障,使行驶安全面临风险,威胁高速动车的正常运行。其中,属于转向架及其辅助系统的轴箱轴承一旦发生故障,很可能造成车轴热切甚至是脱轨,因此研究轴箱轴承数据挖掘模型和监控预警技术对于保证列车安全运行具有重要意义。通过研究动车组实时轴承温度与外温、速度、轮对里程、轴承位置等特征之间的关系,分析产生轴温过高的原因,并基于RBF神经网络对轴箱轴承的温度建立预测模型,进而设计故障预警系统,为日后的检修维护工作提供指导。
Many device faults of EMU train can cause terrible effects while running which has been a serious threaten to EMU train's operating security. The axlebox bearing unit which belongs to bogie and its assistance subsystem,can cause axle broken or even carriage derailed when it has fault. The study of fault alarming of axlebox bearing unit based on data-mining model has great significance for EMU trains operating security. This paper investigated the cause of temperature rise by analyzing the correlation among the temperature of axlebox bearing unit with outdoor temperature,train speed,running kilometer,bearing location,etc.,and built an axlebox bearing unit's temperature predicting model based on RBF neural network. The fault alarming system based on the temperature predicting model can be a guidance for care and maintenance.
作者
王远霏
孙海荣
裴春兴
陈永春
刘先升
WANG Yuanfei;SUN Hairong;PEI Chunxing;CHEN Yongchun;LIU Xiansheng(Product Technical Research Center,Tangshan Railway Vehicle Co.,Ltd.,Tangshan 063035 Hebei,China)
出处
《铁道机车车辆》
北大核心
2019年第3期43-48,63,共7页
Railway Locomotive & Car
