基于密度聚类算法和广度优先搜索算法的道岔摩擦电流智能分析系统
邱晓莉1韩思远2熊庆1,3,4余东1
Intelligent Analysis System for Turnout Friction Current Based on Density Clustering Algorithm and Breadth-first Search Algorithm
QIU Xiaoli1HAN Siyuan2XIONG Qing1,3,4YU Dong1
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作者信息:1.成都工业职业技术学院智能制造与汽车学院， 610213， 成都
2.广西交控智维科技发展有限公司， 530219， 南宁
3.西华大学流体及动力机械教育部重点实验室， 610213， 成都
4.西华大学汽车测控与安全四川省重点实验室， 610213， 成都
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Affiliation:1.School of Intelligent Manufacturing and Automobile, Chengdu Industrial Vocational and Technical College, 610213, Chengdu, China
2.Guangxi Traffic Control Intelligent Maintenance Technology Development Co., Ltd., 530219, Nanning, China
3.Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, 610213, Chengdu, China
4.Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, 610213, Chengdu, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.04.022
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中图分类号/CLCN:TP3：U231.7
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栏目/Col:研究报告
摘要:
［目的］现场的道岔摩擦电流测试与调整存在流程繁琐且风险高、对检修人员专业水平要求高、测定数值的主观性占比大3个弊端，为此需要基于各类智能算法及技术提升道岔的智能运维水平。［方法］分析了道岔摩擦电流测试曲线4个阶段的特征，提出建立道岔摩擦电流的智能分析系统。阐述了该系统的功能及工作原理，设定了该系统的摩擦电流标准值及阈值范围。该系统可基于密度聚类算法和广度优先搜索算法自动获取道岔摩擦电流值。介绍了该系统的调试界面截图，以说明系统在获取道岔摩擦电流值如何为现场检修人员提供操作建议。［结果及结论］该智能系统具有良好的可用性，实现了节约检修时间、降低维护成本和提高检修效率的既定目的。
Abstracts:
［Objective］ The turnout friction current on-site testing and adjustment involve three major unfavorable aspects of complex processes and high risks, high professional requirements for maintenance personnel, and significant subjective component in determining numerical values, calling for enhancement of the intelligent operation and maintenance level of turnout based on various intelligent algorithms and technologies. ［Method］ The characteristics of the four stages of turnout friction current testing curve are analyzed, and an intelligent analysis system for turnout friction current is proposed. The functionality and working principle of the system are elucidated, and standard values and threshold ranges for friction current are established. Based on density clustering algorithm and breadth-first search algorithm, the system can automatically retrieve the turnout friction current value. Screenshots of the system debugging interface are presented to illustrate how the system provides operational suggestions for on-site maintenance personnel in obtaining turnout frictional current values. ［Result & Conclusion］ This intelligent system demonstrates excellent usability and achieves the predefined goals of saving maintenance time, reducing maintenance costs, and increasing maintenance efficiency.
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