基于DBSCAN/PSO-SOM的道岔故障诊断

Turnout fault diagnosis based on DBSCAN/PSO-SOM

为诊断铁路道岔控制电路中的常见故障,提出了一种基于数据密度聚类算法(Density-based spatial clustering of applications with noise,DBSCAN)与自组织特征映射网络(Self-organizing feature map,SOM)结合的诊断方法。利用微机监测系统记录转辙机三相电流曲线,以转辙机动作原理为标准对曲线分段处理并计算三相电流特征参数。针对初始特征维数较高的问题,以DBSCAN算法筛选故障诊断敏感特征,构建诊断敏感特征集。以粒子群优化算法(Particle swarm optimization,PSO)调整SOM网络权值修改规则从而避免网络出现“死神经元”,设计PSO-SOM网络故障分类器并完成待测样本分类诊断。实验表明,该方法在训练样本较少的情况下,能判断道岔控制电路故障模式。与传统SOM网络相比,其故障诊断准确率更高。

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.