[1]贺德强,孙 一,蒙基伟,等.基于IPSO-BP算法的城轨列车轮对故障率预测模型研究[J].控制与信息技术(原大功率变流技术),2019,(01):59-63.[doi:10.13889/j.issn.2096-5427.2019.01.012]
 HE Deqiang,SUN Yi,MENG Jiwei,et al.Research on the Prediction Model of Wheel Set Failure Rate for Urban Rail Trains Based on IPSO-BP Algorithm[J].High Power Converter Technology,2019,(01):59-63.[doi:10.13889/j.issn.2096-5427.2019.01.012]
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基于IPSO-BP算法的城轨列车轮对故障率预测模型研究()
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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:
2019年01期
页码:
59-63
栏目:
故障诊断
出版日期:
2019-02-05

文章信息/Info

Title:
Research on the Prediction Model of Wheel Set Failure Rate for Urban Rail Trains Based on IPSO-BP Algorithm
文章编号:
2096-5427(2019)01-0059-05
作者:
贺德强1孙 一1蒙基伟1刘建仁2
(1.广西大学 机械工程学院,广西 南宁 530004;2. 南宁中车轨道交通装备有限公司, 广西 南宁 530029)
Author(s):
HE Deqiang1SUN Yi1MENG Jiwei1LIU Jianren2
( 1. College of Mechanical Engineering, Guangxi University, Nanning, Guangxi 530004, China; 2. Nanning CRRC Rail Transit Equipment Co., Ltd., Nanning, Guangxi 530029, China )
关键词:
故障率预测IPSO-BP算法人工神经网络城轨车辆轮对维修策略
Keywords:
failure rate prediction IPSO-BP algorithm neural network urban rail vehicle wheel set maintenance strategy
分类号:
TP391.9; U260
DOI:
10.13889/j.issn.2096-5427.2019.01.012
文献标志码:
A
摘要:
为了提高城轨列车轮对故障率的预测精度,文章采用人工神经网络方法代替传统维修策略模型中基于经验的故障率分布显示表达式,以避开故障分布模型的选择;建立了IPSO-BP(improved particle swarm optimization-back propagation)预测模型,并通过与常规的BP(back propagation)及PSO-BP(particle swarm optimization-back propagation)预测模型进行对比来验证其高效性。仿真结果显示,IPSO-BP神经网络模型的预测误差范围为0~5.5%,输出值的相对误差百分比为0~10%,预测精度均优于常规方法,可为预防性维修决策提供理论参考和方法支撑。
Abstract:
In order to improve the prediction accuracy of wheel set failure rate of urban rail trains, the artificial neural network method was used to replace the empirical expression of failure rate distribution in the traditional maintenance strategy model to avoid the manaul selection of fault distribution models. The IPSO-BP (improved particle swarm optimization-back propagation) prediction model was established and compared with the conventional BP (back propagation) and PSO-BP (particle swarm optimization-back propagation) prediction model. Comparisons are made to verify its efficiency. The simulation results show that the prediction error range of IPSO-BP neural network model is 0~5.5%, and the relative error percentage of output value is 0~10%. The prediction accuracy of IPSO-BP neural network model is better than that of conventional methods, which can provide theoretical reference and methodological support for preventive maintenance decision-making.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-09-08
作者简介:贺德强(1973—),男,博士,教授,博士生导师,主要研究方向为列车故障诊断与智能维护。
基金项目:国家自然科学基金项目(51765006);广西自然科学基金重点项目(2017GXNSFDA198012);广西科技攻关项目(桂科攻1598009-6)
更新日期/Last Update: 2019-02-28