基于数据驱动的地铁列车制动系统管路泄漏检测及预警方法
陈美霞1梁师嵩1顾亦豪2郑欢2
Data-driven Detection and Early Alarming Method for Pipeline Leakage of Metro Train Braking System
CHEN MeixiaLIANG ShisongGU YihaoZHENG Huan
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作者信息:1.中车南京浦镇车辆有限公司，210031，南京
2.上海仁童电子科技有限公司，200233，上海
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Affiliation:CRRC Nanjing Puzhen Co., Ltd., 210031, Nanjing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2022.01.022
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中图分类号/CLCN:U270.35
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栏目/Col:研究报告
摘要:
列车制动系统管路泄漏中制动总风管路泄漏和制动缸及相连管路泄漏的检测定位非常困难。介绍了基于数据驱动的地铁列车制动系统管路泄漏的检测及预警方法。该方法使用源于TCMS(列车控制管理系统)的原始数据并对数据进行处理，基于处理后的数据建立模型；采用以异常检测模型和回归模型为基学习器的机器学习模型在空间中确定健康域，并使用模型的不同指标来表征不同位置的泄漏；依据不同指标随时间的变化规律提供泄漏预警信息。针对原始数据采样率及数据传输质量有限的问题，采用了“率”的物理量定义方法和异常数据段剔除方法。测试结果表明，该模型能够监测、定位泄漏位置并提供预警。
Abstracts:
In train braking system pipeline leakage, the detection and allocation of brake main air pipeline leakage, and brake cylinder and associated pipeline leakage are extremely difficult. The data-driven detection and early alarming method of metro vehicle braking system pipeline leakage is introduced. This method obtains raw data from TCMS (train control and management system) and establishes model after processing the data. A machine learning model based on anomaly detection model and regression model is adopted to determine the health field in the space, and different parameters of the model are used to characterize the leakage at different locations. Early warning information about leakage is provided according to the variation law of different parameters over time. In view of the problem of limited sampling rate of raw data and quality of data transmission, the physical quantity definition method of ‘ratio’ and abnormal data segment elimination method are adopted. The test results show that the model can monitor and locate leakage points and provide early warning.
引文 / Ref:
陈美霞,梁师嵩,顾亦豪,等.基于数据驱动的地铁列车制动系统管路泄漏检测及预警方法[J].城市轨道交通研究，2022，25（1）：113.
CHEN Meixia,LIANG Shisong,GU Yihao,et al.Data-driven Detection and Early Alarming Method for Pipeline Leakage of Metro Train Braking System[J].Urban mass transit，2022，25（1）：113.
CHEN Meixia,LIANG Shisong,GU Yihao,et al.Data-driven Detection and Early Alarming Method for Pipeline Leakage of Metro Train Braking System[J].Urban mass transit，2022，25（1）：113.
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