城市轨道交通车辆受电弓羊角异常状态检测方法
白佳凯李明航周于翔赵梦栓刘斐然
Abnormal Condition Detection Method for Urban Rail Vehicle Pantograph Horn
BAI JiakaiLI MinghangZHOU YuxiangZHAO MengshuanLIU Feiran
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作者信息:中国铁道科学研究院集团有限公司城市轨道交通中心， 100081， 北京
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Affiliation:Urban Rail Transit Center， China Academy of Railway Sciences Co.， Ltd.， 100081， Beijing， China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20252085
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中图分类号/CLCN:U225
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栏目/Col:供电与能源
摘要:
［目的］ 城市轨道交通车辆接触网-受电弓系统作为电客车的重要电力供给载体，其关键部位受电弓羊角的结构可靠性将直接影响列车安全运营。由于传统人工巡检存在检测效率低、检测实时性较差等问题，故采用智能化识别技术对受电弓羊角开展异常状态实时检测意义重大。［方法］ 提出一种基于深度学习及支持向量机技术对受电弓羊角进行异常状态识别的智能检测方法，即利用车载接触网运行状态检测装置获取受电弓实时图像后，采用YOLOv5模型与U-net模型实现复杂环境下受电弓精确定位及羊角图像分割，然后利用支持向量机算法对所提取的特征进行分类，由此判断羊角是否存在异常状态。［结果及结论］ 经验证，上述方法在列车实际运行时可有效检测受电弓羊角的异常状态，检测精确率及召回率皆超90%，可大幅提高受电弓结构异常状态的检测效率，具有理想的实用价值和推广前景。
Abstracts:
［Objective］ Rail transit vehicle catenary-pantograph system is an important power supply media for electric bus. The structural integrity of its critical component pantograph horn directly impacts train operational safety.Traditional manual inspections face efficiency and real-time limitations， driving the demand for intelligent anomaly detection. ［Method］ An intelligent detection method based on deep learning and SVM （support vector machine） techniques is proposed to identify pantograph horn abnormal conditions. After obtaining real-time images of the pantograph horn via onboard catenary operation condition monitoring devices， the YOLOv5 model and U-net model are used for accurate pantograph localization and pantograph horn image segmentation under complex environmental conditions. Subsequently， the extracted features are classified using an SVM algorithm to determine whether the horn is in an abnormal state. ［Result Conclusion］ Validation results show that the proposed method can effectively detect abnormal conditions of the pantograph horn during actual train operation， with both precision and recall rate of detection exceeding 90%. This significantly enhances the efficiency of detecting structural abnormal conditions in pantograph systems and demonstrates promising practical value and potential for widespread application.
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