基于轴箱振动与动力学模型驱动的高速列车车轮失圆状态识别方法

Identification Method of Wheel Out-of-roundness State of High-speed Train Based on Axle Box Vibration and Dynamic Model

针对高速列车车轮失圆识别难以兼顾效率与精度问题,提出一种基于轴箱振动与动力学模型的高速列车车轮失圆状态智能识别方法。首先,利用静态检测设备采集车轮非圆原始数据,提出一种数据增强技术构建车轮非圆增强数据集。其次,将增强数据集输入至高速列车车辆—轨道耦合动力学模型,获取车轮不同失圆状态下轴箱振动样本集。最后,通过构建恰当结构与配置参数的一维卷积神经网络(1-dimensional convolutional neural network,1-DCNN),可对轴箱振动信号进行自适应特征提取,实现对车轮失圆状态的智能识别分类。结果表明:提出的车轮失圆状态智能识别方法能实现正常车轮、多边形车轮、擦伤车轮、随机非圆化车轮与局部缺陷车轮5类车轮失圆状态的智能分类,准确率达99.2%(标准差为0.05),且单个样本平均识别耗时为0.4 ms。结合现场试验,所提方法对实测轴箱振动具有较好识别能力,测试精度为95%。与经典的SVM和BP神经网络相比,1-DCNN模型具有更高的识别准确度。

It is difficult to consider both efficiency and precision in traditional methods to detect wheel out-of-roundness(OOR)of high-speed train.An intelligent identification method based on the axle box vibration and dynamic model is proposed.Firstly,the original data of wheel OOR is collected by a static detection device,and a data enhancement technique is proposed to construct the enhanced data set of wheel OOR.Then,the enhanced data set serves as input to the vehicle-track coupled dynamics model of high-speed train to obtain the vibration sample set of axle box under the excitation of different types of wheel OOR.Finally,the features of axle box vibration signal are adaptively extracted to identify and classify wheel OOR types using a 1-dimensional convolutional neural network(1-DCNN)with appropriate structure and configuration parameters.The results show that the proposed method can intelligently classify five types of wheel OOR,including normal,polygonal,scratched,stochastic non-roundness and local defect wheels.The accuracy rate is 99.2%(standard deviation of 0.05)and the average identification time of a single sample is 0.4 ms.Combined with the field test,the proposed method has a good recognition ability for the measured axle box vibration,and the test accuracy is 95%.Compared with the Support Vector Machine(SVM)and Back Propagation(BP)neural network methods,the 1-DCNN model has higher accuracy.