基于数据挖掘的铁路信号设备故障自动诊断研究

Research on automatic fault diagnosis of railway signal equipment based on Data Mining

铁路信号设备故障的类型多,故障难以识别,导致当前铁路信号设备故障诊断方法存在一定的弊端,如错误诊断率高、诊断实时性差。为了获得更加理想的铁路信号设备故障结果,设计了基于数据挖掘的铁路信号设备故障自动诊断方法。首先分析当前铁路信号设备故障诊断的研究进展,找到引起铁路信号设备故障效果差的原因;然后采集铁路信号设备状态信号,提取铁路信号设备工作状态特征,并引入数据挖掘技术对铁路信号设备故障变化特点进行建模,构建铁路信号设备工作状态的分类器,从而实现铁路信号设备故障诊断;最后选择几种经典铁路信号设备故障诊断方法并进行对比测试。结果表明,相对于经典方法,该方法克服了当前铁路信号设备故障方法存在的局限性,铁路信号设备故障诊断的准确性也得到了明显改善,铁路信号设备故障时间明显缩短。

There are many types of railway signal equipment faults,which are difficult to identify,which leads to some disadvantages of current railway signal equipment fault diagnosis methods,such as high fault misdiagnosis rate and poor real-time diagnosis.In order to obtain more ideal results of railway signal equipment faults,an automatic fault diagnosis method of railway signal equipment based on data mining is designed.Firstly,the research progress of fault diagnosis of railway signal equipment is analyzed to find out the cause of poor fault effect of railway signal equipment.Then,the state signal of railway signal equipment is collected to extract the working state characteristics of railway signal equipment,and the data mining technology is introduced to model the fault change characteristics of railway signal equipment,and a classifier of working state of railway signal equipment is constructed.In order to realize the fault diagnosis of railway signal equipment,several classical methods of fault diagnosis of railway signal equipment are selected and tested.The results show that compared with the classical methods,this method overcomes the limitations of current railway signal equipment fault methods,improves the accuracy of railway signal equipment fault diagnosis,and shortens the fault time of railway signal equipment.