[1]张全明,邓亚波.基于神经网络的机车牵引系统故障诊断研究[J].控制与信息技术(原大功率变流技术),2018,(03):74-77.[doi:10.13889/j.issn.2096-5427.2018.03.016]
 ZHANG Quanming,DENG Yabo.Locomotive Traction Malfunction Diagnosing System with Neural Network[J].High Power Converter Technology,2018,(03):74-77.[doi:10.13889/j.issn.2096-5427.2018.03.016]
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基于神经网络的机车牵引系统故障诊断研究()
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《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2018年03期
页码:
74-77
栏目:
故障诊断
出版日期:
2018-06-05

文章信息/Info

Title:
Locomotive Traction Malfunction Diagnosing System with Neural Network
文章编号:
2096-5427(2018)03-0074-04
作者:
张全明 1邓亚波2
(1. 中国神华能源股份有限公司神朔铁路分公司,陕西神木 719316;2. 株洲中车时代电气股份有限公司,湖南株洲 412001)
Author(s):
ZHANG Quanming1 DENG Yabo2
( 1. Shenshuo Railway Branch, China Shenhua Energy Incorporated Company, Shenmu, Shaanxi 719316, China; 2. Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou, Hunan 412001, China )
关键词:
故障诊断神经网络径向基函数机车牵引系统
Keywords:
malfunction diagnosing neural network radial basis function locomotive traction system
分类号:
TP273+.5
DOI:
10.13889/j.issn.2096-5427.2018.03.016
文献标志码:
A
摘要:
为实现故障的准确定位,针对“神华号”电力机车牵引系统,提出了一种基于径向基函数神经网络的故障诊断方法:分别采集机车牵引系统正常与故障时的数据,分类处理后作为训练样本,建立用于机车牵引系统故障诊断的径向基函数神经网络模型。故障定位的实验结果验证了该诊断方法的可行性,现场应用也达到了预期效果。
Abstract:
In order to locate faults accurately, a diagnosing method applying neural network was proposed for the malfunction diagnosis of Shenhua electric locomotive traction system. Locomotive traction system data was collected and preprocessed, including those under normal and failure mode, which was used as samples to build RBFNN for locomotive traction malfunction diagnosis. Results of the location of fault in lab verified the feasibility of the diagnosis method, and application in the locomotive maintenance also achieved the expected result.

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备注/Memo

备注/Memo:
收稿日期:2017-11-01
作者简介:张全明(1974-),男,工程师,主要从事机务段技术管理工作。
更新日期/Last Update: 2018-06-26