基于GM(1,1)-IPSO-BP的重载铁路小半径曲线钢轨磨耗预测方法

Prediction method for rail wear of small radius curves on heavy duty railway based on GM(1,1)-IPSO-BP

为实现重载铁路小半径曲线段钢轨磨耗量的精准预测,提出一种非等间距灰色模型GM(1,1)与改进粒子群算法(IPSO)优化BP神经网络相结合的钢轨磨耗预测方法。首先,根据积分原理优化GM(1,1)非等间距模型的背景值计算方法,基于改进的模型得到实测磨耗序列的初步预测结果;然后,利用IPSO算法对BP神经网络的权值和阈值进行自动寻优,对GM(1,1)模型初步预测序列的残差进行校正;最后,将优化后的两种模型组合构建基于GM(1,1)-IPSO-BP的重载铁路小半径曲线地段钢轨磨耗量预测模型。以某重载铁路桥上半径400 m曲线为例,利用长期的磨耗监测数据进行方法的适用性分析,研究结果表明:GM(1,1)-IPSO-BP模型克服了磨耗数据的非线性、随机性特征对计算结果的影响,预测精度优于单独使用GM(1,1)、IPSO-BP模型;背景值优化后的GM(1,1)模型预测准确性更可靠;IPSO优化算法提高了BP神经网络计算的精度和速度;预测结果和实测数据之间的相对误差不大于4%;在预测区间上的绝对误差小于0.4 mm,运用该方法能够较准确地得到钢轨磨耗的发展规律。研究结果可为重载铁路小半径曲线钢轨的精准维修和科学使用提供参考。

In order to achieve accurate prediction of rail wear on small radius curve of heavy-duty railway,a prediction model for rail wear combining improved particle swarm optimization with BP neural network and the GM(1,1)model is proposed.First,the calculation method of background value of is optimized according to the principle of integration,and preliminary prediction results of wear are obtained based on the improved model.Then,the weights and thresholds in network structure nodes of the BP model is optimized by using the IPSO algorithm,and the residual values of the preliminary prediction results are corrected.Finally,the optimized two models are combined to construct a prediction model for rail wear in small radius curve of heavy-duty railways based on GM(1,1)-IPSO-BP model.Taking a curve with 400 m radius on a Heavy Duty Railway bridge as a case study,applicability analysis of the method in this article is analyzed using long-term wear monitoring data.The results show that the GM(1,1)-IPSO-BP model overcomes the influence of nonlinear and random characteristics of wear data on the calculation results,and its prediction accuracy is superior to using GM(1,1)and IPSO-BP alone.The GM(1,1)model with optimized background values has more reliable prediction accuracy.Prediction accuracy and calculation speed of the BP network optimized by the IPSO algorithm is improved.The relative error between the predictive value calculated with this model and the detection value is less than 4%,and the absolute error of abrasion value is not more than 0.4 mm.Through this method,the development law of rail wear can be accurately obtained.The research results could provide reference for the precise maintenance and scientific use of small radius curve rail in heavy haul railways.