[1]王远霏,孙海荣,裴春兴,等.CRH380BL型动车组受电弓系统可靠性分析[J].机车电传动,2019,(04):77-83.[doi:10.13890/j.issn.1000-128x.2019.04.017]
 WANG Yuanfei,SUN Hairong,PEI Chunxing,et al.System Reliability Analysis of CRH380BL EMUs Pantograph[J].Electric Drive for Locomotives,2019,(04):77-83.[doi:10.13890/j.issn.1000-128x.2019.04.017]
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CRH380BL型动车组受电弓系统可靠性分析()
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机车电传动[ISSN:1000-128X/CN:43-1125/U]

卷:
期数:
2019年04期
页码:
77-83
栏目:
研究开发
出版日期:
2019-07-10

文章信息/Info

Title:
System Reliability Analysis of CRH380BL EMUs Pantograph
文章编号:
1000-128X(2019)04-0077-07
作者:
王远霏孙海荣裴春兴蒋洁刘先升
(中车唐山机车车辆有限公司技术研究中心,河北 唐山 063035)
Author(s):
WANG Yuanfei SUN Hairong PEI Chunxing JIANG Jie LIU Xiansheng
( Technology Research Center, CRRC Tangshan Co., Ltd., Tangshan, Hebei 063035, China )
关键词:
动车组受电弓可靠性分析故障分布故障预警数据分析
Keywords:
EMUs pantograph reliability analysis fault distribution fault alarm data analysis
分类号:
U266.2;U264.3+4
DOI:
10.13890/j.issn.1000-128x.2019.04.017
文献标志码:
A
摘要:
基于CRH380BL型动车组受电弓的历史故障数据,利用可靠性数据分析方法,针对4种主要故障零件,研究其故障分布规律,进而评估受电弓整个系统的可靠性水平。在受电弓零部件中发生故障的主要是ADD及供风管、供风管路、10-X01继电器和升弓气囊4个部分。研究表明,ADD及供风管与10-X01继电器的故障分布规律为两参数威布尔分布,供风管路与升弓气囊的故障分布规律为指数分布。利用可靠性分析的方法找出受电弓系统可靠性的薄弱环节,为动车组故障检修与预警提供理论依据。
Abstract:
Based on the historical fault data of pantograph of CRH380BL EMUs, the fault distribution law of four main fault parts was studied by using the method of reliability data analysis, and then the reliability level of pantograph system was evaluated. ADD and air supply pipe, air supply pipe line, 10-X01 relay and pantograph airbag were the four parts that cause the most faults in pantograph. The results showed that the fault distribution of ADD, air supply pipe and 10-X01 relay is two-parameter Weibull distribution, and the fault distribution law of air supply pipe line and bow airbag is exponential distribution. Using the method of reliability analysis, the weak link of pantograph system reliability could be find out, which provided theoretical basis for EMUs fault overhaul and early warning.

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备注/Memo

备注/Memo:
作者简介:王远霏(1979—),男,硕士,工程师,主要研究方向为轨道车辆健康管理技术、关键部件智能诊断及故障预测研究。
更新日期/Last Update: 2019-07-10