基于智能传感器技术的列车轴温监测系统的研究  

Research on train axle temperature monitoring technology based on intelligent sensor technology

作　　者：贾刚 傅霖煌 赖远桥 Jia Gang;Fu Linhuang;Lai Yuanqiao(China Energy Railway Equipment Company Limited Shaanxi Branch,Shaanxi Shenmu,719316,China;Shenzhen Invengo Information Technology Co.,Ltd.,Guangdong Shenzhen,518052,China)

机构地区：[1]国家能源集团铁路装备有限责任公司陕西分公司,陕西神木719316 [2]深圳市远望谷信息技术股份有限公司,广东深圳518052

出　　处：《机械设计与制造工程》2021年第10期59-62,共4页Machine Design and Manufacturing Engineering

摘　　要：提出了一种基于智能传感器技术的铁路轴温监测系统。引入灰度模型算法结合最小二乘法偏移原理,提出了融合权重参数的二乘灰度模型列车轴温分析及预警算法,有效解决了原始算法于极值点大概率误差过大的问题,提升了短时间预测的准确度。对比实验结果显示,该算法可以有效监测列车轴温,故障预警的准确率对比其他算法平均提高了7%左右,平均误差小于2%,具备较高的工程研究及应用推广价值。A railway axle temperature monitoring technology based on intelligent sensor technology is proposed.By collecting the changes of axle temperature in real time,introducing the gray model algorithm and the least square offset principle,a double gray model train axle temperature analysis and early warning algorithm integrating weight parameters is established.The algorithm has small samples and high execution efficiency.It effectively solves the problem that the probability error of the original algorithm at the extreme point is too large,and improves the accuracy of short-time prediction.Comparative experiments show that the algorithm proposed in this paper can effectively monitor the train axle temperature.Compared with other algorithms,the accuracy of fault early warning is improved by about 7%on average,and the average error is less than 2%.The effect is improved obviously.It has high engineering research and application value.

关 键 词：货运列车 灰度模型 最小二乘法 轴温监测 故障预警 

分 类 号：U272.2[机械工程—车辆工程]