[1]梅欢[] [],马艳东[] [],单九思[],等. 基于样条插值与RBF网络的道岔故障诊断系统[J].计算机技术与发展,2017,27(05):160-163.
　MEI Huan[][],MA Yan-dong[] [],SHAN Jiu-si[],et al. Research on Switch Fault Diagnosis System with Cubic SplineInterpolation and RBF Neural Network[J].,2017,27(05):160-163.
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 基于样条插值与RBF网络的道岔故障诊断系统()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年05期
页码:
160-163
栏目:
应用开发研究
出版日期:
2017-05-10

文章信息/Info

Title:
 Research on Switch Fault Diagnosis System with Cubic SplineInterpolation and RBF Neural Network
文章编号:
1673-629X（2017)05-0160-04
作者:
 梅欢[1] [2]; 马艳东[1] [2]; 单九思[3] ; 彭晔[3]
 1.河北省科学院 应用数学研究所；2.河北省信息安全认证工程技术研究中心；3.石家庄铁道大学
Author(s):
 MEI Huan[1][2];  MA Yan-dong[1] [2]; SHAN Jiu-si[3] ; PENG Ye[3]
关键词:
 ; 道岔故障诊断; 人工智能; RBF神经网络; 三次样条插值; 道岔动作电流
Keywords:
 ; switch fault diagnosis; artificial intelligence; RBF neural network; Cubic Spline Interpolation; electric curves of switch action
分类号:
TP182
文献标志码:
A
摘要:
 随着列车运行速度与行车密度的不断提高,道岔将面临更加严峻的考验,而传统依靠维护人员研读相关监测数据进行故障诊断的手段,越来越不能够适应铁路对运行安全的高要求.为快速、准确诊断出道岔故障,特建立基于三次样条插值与RBF神经网络的智能道岔故障诊断模型.利用基于三次样条插值的数据整合模块将不同维数的道岔动作电流数据划归成统一的数据维数.采用新型RBF神经网络对其进行故障诊断.利用某火车站道岔动作的真实历史监测数据对所提模型的有效性与可行性进行验证.实验结果表明,所提出的模型不仅能够适应不同数据维数的道岔动作曲线数据,而且还可以快速、准确地对道岔故障进行诊断,从而帮助维护人员缩短故障处理时间,提高铁路行车的安全性.
Abstract:
The train rail switch is facing more demanding request with the increasing speed and traffic density.The traditional fault diagnosis method which relies on maintenance personnel monitoring data cannot meet the higher safety request for the train operation safety.In order to diagnose the faults of the rail switch rapidly and accurately,the new intelligent rail switch fault diagnosis model based on Cubic Spline Interpolation and RBF neural network is established.The data integration module based on Cubic Spline Interpolation has been used to normalize the electric current curves of the rail switch into the same dimension.Then,the new RBF neural network is used to diagnose the new data of the electric current curves of the rail switch with same dimension.The model has been validated and evaluated with the historical and real monitoring data from certain railway station.The experiment results show that this model can not only be used to process the electric current curves of the rail switch action with different dimensions,but also can be used to diagnose rail switch faults rapidly and accurately.With this model,the fault processing time can be significantly decreased and the railway traffic can be safer.

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更新日期/Last Update: 2017-07-07