YANG Juhua1， LI Xutong1,2， XING Dongfeng 2,3， CHEN Guangwu 2,3
（1. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China；2. Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China； 3. Automatic Control Research Institute, Lanzhou Jiao tong University, Lanzhou 730070, China）
Abstract： 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.
Key words： turnout； fault diagnosis； density-based spatial clustering of applications with noise (DBSCAN)； particle swarm optimization (PSO)； self-organizing feature map (SOM)
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基于DBSCAN/PSO-SOM的道岔故障诊断
杨菊花1， 李旭彤1,2， 邢东峰2,3， 陈光武2,3
（1. 兰州交通大学 交通运输学院， 甘肃 兰州 730070；2. 甘肃省高原交通信息工程及控制重点实验室， 甘肃 兰州 730070； 3. 兰州交通大学 自动控制研究所， 甘肃 兰州 730070）
摘要: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。
关键词:道岔； 故障诊断； 基于数据密度聚类算法； 粒子群算法； 自组织特征映射
引用格式：YANG Juhua， LI Xutong， XING Dongfeng, et al． Turnout fault diagnosis based on DBSCAN/PSO-SOM． Journal of Measurement Science and Instrumentation， 2022， 13（3）： 371-378. DOI： 10．3969／j．issn．1674-8042．2022．03．012
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