[1]王 雨,吕 宇,赵沐华.基于递推最小二乘法的地铁车辆减振器在线诊断技术[J].控制与信息技术(原大功率变流技术),2018,(03):68-73.[doi:10.13889/j.issn.2096-5427.2018.03.200]
 WANG Yu,LYU Yu,ZHAO Muhua.On-line Diagnosis Technology for the Shock Absorbers on Subway Vehicle Based on RLS[J].High Power Converter Technology,2018,(03):68-73.[doi:10.13889/j.issn.2096-5427.2018.03.200]
点击复制

基于递推最小二乘法的地铁车辆减振器在线诊断技术()
分享到:

《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:
68-73
栏目:
故障诊断
出版日期:
2018-06-05

文章信息/Info

Title:
On-line Diagnosis Technology for the Shock Absorbers on Subway Vehicle Based on RLS
文章编号:
2096-5427(2018)03-0068-06
作者:
王 雨吕 宇赵沐华
(株洲中车时代软件技术有限公司,湖南株洲 412001)
Author(s):
WANG Yu LYU Yu ZHAO Muhua
( Zhuzhou CRRC Times Software Technology Co., Ltd., Zhuzhou, Hunan 412001, China )
关键词:
地铁车辆悬挂部件减振器FMECARLS在线诊断
Keywords:
suspension parts of subway bogie shock absorber FMECA RLS on-line diagnosis
分类号:
U269.32+2
DOI:
10.13889/j.issn.2096-5427.2018.03.200
文献标志码:
A
摘要:
为保障列车运行安全性并避免减振器过度维修,文章对地铁车辆在线监测技术进行研究,针对车辆垂向减振器提出了基于振动数据的递推最小二乘法(RLS)的过程参数评估模型和车辆的“输入- 输出”模型,并通过构建地铁车辆的多体动力学模型以提取模型计算的输入/ 输出参数,从而实现减振器参数的识别;借助FMECA 分析方法研究地铁车辆悬挂部件的失效模式和部件之间的可靠性框图,分析各悬挂部件失效模式之间的关联关系。诊断结果表明,RLS 算法在进行减振器在线参数辨识时具有比较好的跟踪收敛效果。
Abstract:
To ensure the safety of train operation and avoid excessive maintenance of shock absorbers, based on the vibration data, a process parameter evaluation model of the recursive least squares (RLS) and a input-output model were presented according to the research of the metro vehicle on-line monitoring technology. By means of computer simulation, a multi-body dynamics model of metro vehicle was constructed, and input parameters and output parameters of the model were extracted to identify the parameters of the shock absorber. By means of FMECA analysis method, the failure mode of subway suspension components and the block diagram of reliability between parts were studied, and the correlation between failure modes of suspension components was analyzed. Diagnostic results show that the RLS algorithm has better tracking convergence effect in the shock absorber online parameter identification.

参考文献/References:

[1]李艳. 高速列车动力学参数影响度的研究与应用[D]. 成都:西南交通大学,2013.
[2]任宁. 列车悬挂系统的性能监测[D]. 杭州:浙江大学,2014.
[3]时旺,孙宇锋,王自力,等. PHM 系统及其故障预测模型研究[J]. 火力与指挥控制,2009,34(10):29-35.
SHI W, SUN Y F, WANG Z L, et al. A Study of PHM System and Its Fault Forecasting Model[J]. Fire Control & Command Control, 2009,34(10):29-35.
[4]曾声奎,Michael G Pecht,吴际. 故障预测与健康管理(PHM) 技术的现状与发展[J]. 航空学报,2005,26(5):626-632.
 ZENG S K, PECHT M G. WU J. Status and Perspectives of Prognostics and Health Management Technologies[J]. Acta Aeronautica ET Astronautica Sinica, 2005, 26(5):626-632.
[5]李楠. 故障预测与健康管理(PHM) 在ERP 系统中的应用研究[D]. 北京:中国科学院大学,2013.
[6]杨晓东. 铁道车辆抗蛇行减振器动态特性研究[D]. 成都:西南交通大学,2015.
[7]秦剑生. 基于物理参数的抗蛇行减振器力学模型研究[D]. 成都:西南交通大学,2015.
[8]贾洪龙,宋春元,强锋. 铁道车辆减振器漏油故障与内部特性分析[J]. 铁道机车车辆, 2015,35(6):32-35.
 JIA H L, SONG C Y, QIANG F. Analysis of Leakage and Internal Characteristics of Railway Vehicle Hydraulic Damper[J]. Railway Locomotive & Car, 2015, 35(6):32-35.
[9]石敏,吴正国,尹为民. 基于RLS 算法的时变谐波检测[J]. 电工技术学报,2005,20(1):50-53.
SHI M, WU Z G, YIN W M. Time-Varying Harmonic Detection Ba s e d o n RLS Al g o r i t hm[J]. Tr a n s a c t i o n s o f Ch i n a Electrotechnical Society, 2005, 20(1):50-53.
[10]韩伟,王大志,刘震. 基于可变遗忘因子RLS 算法的谐波电流检测方法[J]. 电工技术学报,2013,28(12):70-74.
HAN W, WANG D Z, LIU Z. A Harmonic Current Detection Method Based on Variable Forgetting Factor RLS Algorithm[J]. Transactions of China Electrotechnical Society, 2013, 28(12):70-74.
[11]邓自立,王欣,高媛. 建模与评估[M]. 北京:科学出版社, 2007.
[12]丁建明. 车辆动力学性能参数估计方法研究[D]. 成都:西南交通大学,2007.
[13]LIU X Y, ALFI S, BRUNI S. An efficient recursive least squarebased condition monitoring approach for a rail vehicle suspension system[J]. Vehicle System Dynamics, 2016, 54(6):814-830.
[14]方崇智,萧德云. 过程辨识[M]. 北京:清华大学出版社, 1988.

备注/Memo

备注/Memo:
收稿日期:2017-12-29
作者简介:王雨(1982-),男,高级工程师,主要研究方向为轨道交通信息化与智能运维。
基金项目:国家重点研发计划(2016YFB1200401)
更新日期/Last Update: 2018-06-26