基于多车型CNN-GRU性能预测模型的轨道状态评价

不同车型高速综合检测列车的动力学传递特性不同，使得其对同一线路的车体加速度评价结果存在一定差异.为解决上述问题，本文基于多列动检车的检测数据，将卷积神经网络（convolutional neural network,CNN）与门控循环单元（gated recurrent unit,GRU)相结合，建立了多车型车辆动力学响应预测模型，通过输入多项实测轨道不平顺和车速预测各车型的车体垂向和横向加速度，并将多车型车体加速度预测值的最大包络作为轨道状态评价依据.结果表明：将高低、轨向不平顺等8项轨道不平顺和车速共同作为输入参数的模型预测性能最优，车体垂向和横向加速度预测的评估指标分别提升了5%～13%和25%～36%;CNN-GRU模型所预测的车体加速度在时域和频域均与实测结果吻合较好，相关系数最大达到0.902；且相比于BP (back propagation)神经网络，各项车体垂向和横向加速度预测的评估指标分别提升了36%～109%和11%～167%；针对某轨道几何状态不良区段应用效果，预测6种车型中有4种车型达到车体垂向加速度Ⅰ级或Ⅱ级超限，有1种车型达到车体横向加速度Ⅰ级超限，提高了轨...

Track Condition Evaluation for Multi-vehicle Performance Prediction Model Based on Convolutional Neural Network and Gated Recurrent Unit

The dynamic transmission characteristics of different types of high-speed track inspection vehicles are different, which makes the evaluation results of vehicle body acceleration on the same railway line different. To solve the above problem, the convolutional neural network (CNN) is combined with the gated recurrent unit (GRU) to establish a dynamic response prediction model for multi-vehicle dynamic response, which predicts the vertical and lateral acceleration of each vehicle by inputting a number of measured track irregularities and vehicle speeds, and uses the maximum envelope of the predicted values of multi-vehicle acceleration as the basis for track state evaluation. The results show that the model with eight track irregularities and vehicle speed, such as longitudinal irregularity, horizontal irregularity, as input parameters has the best prediction performance, and the evaluation indices of vertical and lateral vehicle acceleration prediction are increased by 5%–13% and 25%–36%, respectively. The vehicle acceleration predicted by the CNN-GRU model is in good agreement with the measured results in both time domain and frequency domains, with the maximum correlation coefficient of 0.902. Compared with back propagation (BP) neural network, CNN-GRU improves the evaluation indices of vertical and lateral vehicle acceleration prediction by 36%–109% and 11%–167%, respectively. The application result in a section with poor track geometry state shows that four out of the six vehicle types reach the level Ⅰ or Ⅱ overrun of the vehicle vertical acceleration, and one vehicle type reaches the level Ⅰ overrun of the vehicle lateral acceleration, which improves the accuracy and consistency of the track state evaluation.