Turnout fault diagnosis based on DBSCAN/PSO-SOM  

基于DBSCAN/PSO-SOM的道岔故障诊断

作　　者：YANG Juhua LI Xutong XING Dongfeng CHEN Guangwu 杨菊花;李旭彤;邢东峰;陈光武(兰州交通大学交通运输学院,甘肃兰州730070;甘肃省高原交通信息工程及控制重点实验室,甘肃兰州730070;兰州交通大学自动控制研究所,甘肃兰州730070)

机构地区：[1]School of Traf fic 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 Uhiversity,Lanzhou 730070,China

出　　处：《Journal of Measurement Science and Instrumentation》2022年第3期371-378,共8页测试科学与仪器（英文版）

基　　金：High Education Research Project Funding(No.2018C-11);Natural Science Fund of Gansu Province(Nos.18JR3RA107,1610RJYA034);Key Research and Development Program of Gansu Province(No.17YF1WA 158)。

摘　　要：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.

关 键 词：TURNOUT fault diagnosis density-based spatial clustering of applications with noise(DBSCAN) particle swarm optimization(PSO) self-organizing feature map(SOM) 

分 类 号：U216.425[交通运输工程—道路与铁道工程]