|本期目录/Table of Contents|

[1]房楠,朱亚男.列车车轮状态监控系统设计与实现[J].工业仪表与自动化装置,2025,(02):37-41.[doi:10.19950/j.cnki.CN61-1121/TH.2025.02.007]
　FANG Nan,ZHU Yanan.Design and implementation of a train wheel condition monitoring system[J].Industrial Instrumentation & Automation,2025,(02):37-41.[doi:10.19950/j.cnki.CN61-1121/TH.2025.02.007]
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列车车轮状态监控系统设计与实现(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2025年02期
页码:
37-41
栏目:
出版日期:
2025-04-15

文章信息/Info

Title:
Design and implementation of a train wheel condition monitoring system
文章编号:
1000-0682（2025）02-0037-05
作者:
房楠; 朱亚男
（西安铁路职业技术学院，陕西 西安 710014）
Author(s):
FANG Nan;  ZHU Ya’nan
(Xi’an Railway Vocational & Technical Institute, Shaanxi Xi’an, 710014, China)
关键词:
列车车轮; 振动状态; 系统设计
Keywords:
train wheel;  vibration status;  system design
分类号:
U269；TP18
DOI:
10.19950/j.cnki.CN61-1121/TH.2025.02.007
文献标志码:
A
摘要:
针对铁路交通行车安全问题，聚焦于列车车轮振动状态数据的实时采集与分析，通过挖掘列车运行规律实现运行工况预测，以保障行车安全。选取列车车轮作为监控对象，构建了实时的车轮状态监控系统，设计了全面的系统架构和数据处理方法。通过LabVIEW编程实现了实时数据采集、处理、存储与展示等功能，同时采用最小二乘拟合法对车轮振动数据进行处理，以提高数据的准确性和可靠性。实验验证表明，系统具有良好的实时性与准确性，为预防行车事故、预判行车状态提供了有力的技术支持，应用前景显著。
Abstract:
This paper focuses on the real-time collection and analysis of train wheel vibration data in relation to railway traffic safety. The aim is to predict operational conditions by exploring train operation patterns, thereby ensuring safety. The study selects train wheels as the monitoring object and constructs a real-time wheel condition monitoring system. A comprehensive system architecture and data processing method are designed. Functions such as real-time data acquisition, processing, storage, and display are achieved through LabVIEW programming. Furthermore, the least squares fitting method is employed to process wheel vibration data, enhancing data accuracy and reliability. Experimental verification demonstrates that the system exhibits excellent real-time performance and accuracy, providing strong technical support for preventing train accidents and predicting train conditions. The research showcases significant application prospects in the field.

参考文献/References:

[1] 张志波,张振先,冯永华.高速动车组转向架综合智能检测技术研究[J].铁道车辆,2021,59(06):40-44.
[2] LI H F,Parikh D,He Q.Improving rail network velocity: A machine learning approach to predictive maintenance[J].Transportation Research Part C-Emerging Technologies, 2014(45):17-26.
[3] 朱亚男,李冰毅,房楠,等.动车组驱动装置故障模式分析及风险评估[J].工业仪表与自动化装置,2022(2): 72-75.
[4] 张龙,张号,周建民,等.采用显式动力学的轴承性能退化评估指标构建[J].西安交通大学学报,2022,56(8): 11-21.
[5] 李震.面向模态分析的多通道同步采集与数据处理系统设计[D].重庆：重庆大学,2008.
[6] 杨乐平.LabVIEW高级程序设计[M].北京:清华大学出版社,2003.
[7] PAN Difu,WANG Mengge,ZHU Yanan,et al.An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune[J]. Journal of central south university,2013(20): 3497-3503.

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备注/Memo

备注/Memo:
收稿日期：2024-08-01基金项目：陕西省教育厅2023年度自然科学类专项科研计划项目“基于改进单质点牵引计算模型的列车停车对标仿真训练系统”（23JK0623）第一作者：房楠(1989—)，女，陕西商洛人，硕士，主要研究方向为机车制动、智能制造技术。E-mail：312271170@qq.com
更新日期/Last Update: 1900-01-01