基于数据分段拟合的受电弓性能检测误差补偿

Error compensation of pantograph performance detection based on data segment fitting

静态接触力是评价受电弓性能的一项重要指标,也是衡量弓网接触质量的重要因素,直接影响着列车行驶安全。传统测量方法存在效率低、精度差等问题,本文基于物联网云、管、端的思路,研发了一种车载式受电弓性能检测装置,并提出了一种自适应分段数据拟合算法,以优化检测精度。该算法将数据分段与曲线模型寻优同时进行,通过逐步扩大区间长度和多项式、指数、高斯、傅里叶、幂函数五种拟合模型的评估,在最优点实现自适应分段。实验表明,该拟合算法使检测装置的平均误差率从1.91%下降至0.21%,优于文中其他对比拟合算法,显示出良好的精度补偿效果。

Static contact force is an important index to evaluate the performance of pantograph,and also an important factor to measure the contact quality of pantograph,which directly affects the safety of train running.Traditional measurement methods have problems such as low efficiency and poor accuracy.Based on the idea of cloud,tube and terminal of the internet of things,this thesis develops a vehicle-mounted pantograph performance detection device,and proposes an adaptive segmental data fitting algorithm to optimize detection accuracy.The algorithm performs data segmentation and curve model optimization at the same time,and realizes adaptive segmentation at the best point by gradually expanding interval length and evaluating five fitting models:polynomial,exponential,Gaussian,Fourier and power function.The experimental results show that the average error rate of the measuring device is reduced from 1.91%to 0.21%,which is better than other matching methods in the thesis and shows a good precision compensation effect.