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作 者:黄迪 李子先 侯招文 刘灿 林波 臧晓斌 HUANG Di;LI Zixian;HOU Zhaowen;LIU Can;LIN Bo;ZANG Xiaobin(Zhuzhou CRRC Times Electric Co.,Ltd.,Zhuzhou,Hunan 412001,China)
机构地区:[1]株洲中车时代电气股份有限公司,湖南株洲412001
出 处:《控制与信息技术》2024年第3期109-114,共6页CONTROL AND INFORMATION TECHNOLOGY
基 金:国家重点研发计划项目(2021YFB3203200)。
摘 要:常规的轴温预报警诊断逻辑主要基于给定温度绝对值阈值及其上升斜率阈值,当被测温度值出现波动时,通常难以进行有效分析和判断。高速动车组轴温监测系统因硬件线路接触不良或电磁干扰等外部因素会导致被测温度异常跳变或缺失,进而引发误报预报警的问题,对此,文章提出了一种异常温度曲线剔除逻辑方案。该方案基于大量现场被测轴温数据分析结果,提取可以准确描述轴温曲线变化的特征量,用于描述其波动情况和上升趋势,以此判断是否存在异常温度变化。仿真结果表明了该方案在识别温度异常跳变方面的有效性和可行性。同时,文中所提方案已完成线路上长期运行考核,现场故障误报率由78.3%降低到26.5%,达到了大幅降低现场误报率的目的。The conventional diagnostic logics for hotbox warning/alarms are mainly based on predefined absolute temperature thresholds and temperature rising slope thresholds.They often fall short in effective analysis and judgments when encountering fluctuating measured temperature values.Abnormal jumps and missing of axle temperature measurements frequently occur on the hotbox monitoring system for high-speed trains due to external factors such as inadequate hardware circuit contact or electromagnetic interference,potentially triggering false warning/alarms.To solve these,this paper proposes a logic framework for removing abnormal temperature curves.Drawing insights from an extensive analysis of field data on axle temperature measurements,this approach enables extracting features that accurately depict variations in temperature curves,to display fluctuations as well as rising trends of temperature data,thereby offering a basis for identifying abnormal temperature variations.Simulation results showcased the effectiveness and viability of the proposed scheme in identifying abnormal jumps in temperature data.Furthermore,the methodology has been assessed through long-time track operation,with a notable reduction in the false warning rate from 78.3%to 26.5%.
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