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

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.