基于改进FNN的道岔电路故障诊断方法  

作　　者：高亚丽 陈光武[1,2] 

机构地区：[1]兰州交通大学自动控制研究所,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

出　　处：《科技创新与应用》2020年第15期125-127,共3页Technology Innovation and Application

摘　　要：道岔是直接关系到高速铁路安全运营的关键信号设备,目前对其控制电路故障诊断方式主要是依靠简单仪器与人的以往经验。针对道岔控制电路故障的模糊性与不确定性特点和实现道岔控制电路故障诊断的高准确率与智能化,文章提出一种基于模糊推理法与模糊神经网络(Fuzzy Neural Network,FNN)相结合的诊断方法:首先在总结S700K道岔控制电路常见的8种故障类型、建立模糊规则和选择隶属函数的基础上,建立基于模糊推理法FNN诊断模型;其次为避免算法收敛慢和陷入局部最优,文章通过引入改进的动态学习速率和附加动量项的学习调整法,实现对算法的改进,并比较算法改进前后的误差曲线;最后将文中算法和BP神经网络进行诊断准确率对比。仿真实验结果表明,该算法对道岔控制电路故障诊断的准确率高并且能达到99.6%以上。Turnout is a key signal device directly related to the safe operation of high-speed railways.At present,the fault diagnosis method of its control circuit mainly relies on the past experience of simple instruments and people.Aiming at the ambiguity and uncertainty of turnout control circuit faults and the high accuracy and intelligence of turnout control circuit fault diagnosis,this paper proposes diagnosis method combiningFuzzy Inference Method and Fuzzy Neural Network(FNN):Firstly,based on the summary of the eight common fault types of S700K turnout control circuit,the establishment of fuzzy rules and the selection of membership functions,a FNN diagnosis model based on fuzzy inference is established;secondly,in order to avoid the algorithm’s slow convergence and falling into local optimum,this paper introduces an improved dynamic learning rate and a learning adjustment method with additional momentum terms,then the algorithm is improved,and the error curves before and after the algorithm improvement are compared.Finally,the diagnostic accuracy of the algorithm in this paper is compared with the BP neural network.Simulation results show that the algorithm has high accuracy for the turnout control circuit fault diagnosis and can reach more than 99.6%.

关 键 词：道岔控制电路 故障诊断 模糊推理法 模糊神经网络 

分 类 号：U284.92[交通运输工程—交通信息工程及控制]