|本期目录/Table of Contents|

[1]侯成凯.轨道交通车辆牵引系统智能运维研究[J].工业仪表与自动化装置,2023,(05):107-111.[doi:10.19950/j.cnki.cn61-1121/th.2023.05.022]
　HOU Chengkai.Research on intelligent operation and maintenance of rail transit vehicle traction system[J].Industrial Instrumentation & Automation,2023,(05):107-111.[doi:10.19950/j.cnki.cn61-1121/th.2023.05.022]
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轨道交通车辆牵引系统智能运维研究

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

卷:
期数:
2023年05期
页码:
107-111
栏目:
出版日期:
2023-10-15

文章信息/Info

Title:
Research on intelligent operation and maintenance of rail transit vehicle traction system
文章编号:
1000-0682（2023）05-0107-05
作者:
侯成凯
郑州地铁集团有限公司，河南 郑州 450047
Author(s):
HOU Chengkai
Zhengzhou Metro Group Co.,Ltd., Henan Zhengzhou 450047, China
关键词:
轨道交通; 故障诊断; 智能运维; BP神经网络; 实时监控
Keywords:
rail transit;  fault diagnosis;  intelligent operation and maintenance;  BP neural network;  real-time monitoring
分类号:
U231
DOI:
10.19950/j.cnki.cn61-1121/th.2023.05.022
文献标志码:
A
摘要:
针对轨道交通车辆牵引系统的检修维护过程中，运维人员工作量繁琐、劳动强度大、运维效率低等问题，运用大数据和人工智能技术，构建轨道交通车辆牵引系统智能运维的整体架构和网络架构，完成6个应用系统和1个基础服务平台功能设计，提出了基于 BP 神经网络的智能决策算法，实现了车辆牵引系统运行设备的智能化管理，极大提高设备运维效率。最后，以车辆牵引系统中散热器为例，建立散热器热阻预测模型，应用BP神经网络智能决策算法，对设备健康状况进行评估，预测结果表明: 系统运行稳定可靠，预测误差小于0.2 ℃，对车辆牵引设备的健康状态能够精准诊断，为运维人员提供数据支撑和决策支持，提高轨道交通车辆牵引系统运行的可靠性具有重要意义。
Abstract:
In response to the problems of cumbersome workload, high labor intensity, and low efficiency of operation and maintenance personnel in the maintenance process of rail transit vehicle traction system, big data and artificial intelligence technologies are used to construct the overall architecture and network architecture of intelligent operation and maintenance of rail transit vehicle traction system. Six application systems and one basic service platform functional designs are completed, and an intelligent decision-making algorithm based on BP neural network is proposed, Realized intelligent management of vehicle traction system operating equipment, greatly improving equipment operation and maintenance efficiency. Finally, taking the radiator in the vehicle traction system as an example, a prediction model for the thermal resistance of the radiator is established. The BP neural network intelligent decision-making algorithm is applied to evaluate the health status of the equipment. The prediction results show that the system operates stably and reliably, with a prediction error of less than 0.2 ℃. It can accurately diagnose the health status of the vehicle traction equipment, providing data support and decision-making support for operation and maintenance personnel, Improving the reliability of the traction system of rail transit vehicles is of great significance.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2023-05-19

第一作者：
侯成凯（1986—），男，河南焦作人，学士，工程师，研究方向为交通运输、机车车辆。
更新日期/Last Update: 1900-01-01