基于高铁轨道不平顺的车轮不圆顺识别模型

为获取高速运行车辆的车轮不圆顺幅值，并进一步研究轨道谱，建立一种基于轨道不平顺的车轮不圆顺幅值快速测量模型. 首先分析了车轮不圆顺在轨道不平顺检测数据中的分布规律，提出车轮不圆顺的密集采样方法，进而建立基于稀疏轨道不平顺数据的车轮不圆顺动态识别模型. 通过数值仿真研究发现:车轮不圆顺对基于惯性基准法测得的离散轨道不平顺数据的幅值影响较小，对频域（轨道谱）影响较大；车轮不圆顺会干扰波长小于和等于轮长的轨道不平顺检测数据，且对前者影响更大；车轮不圆顺对波长大于轮长的轨道不平顺数据也有影响，最大影响波长仅与车轮周长和轨道不平顺的采样间距有关；识别模型能有效地从轨道不平顺检测数据中提取车轮不圆顺，误差可控制在0.02 mm以内. 

Polygonal Wheel Detection Model Based on Track Irregularity of High-Speed Railways

To detect the amplitudes of polygonal wheels of high-speed railways and better understand the track spectrum, a model based on track irregularity was established for rapidly measuring the amplitude of polygonal wheels. Firstly, the amplitude distribution of polygonal wheels was analyzed within track irregularity inspection data and intensive sampling method (ISM) was proposed. The dynamic detection model of polygonal wheels was established on the basis of the sparse sampling data of track irregularity. The results of numerical simulation show that the polygonal wheel has little influence on the amplitude of the discrete track irregularity data obtained by the inertial reference method, but it has great influence on the frequency domain of track irregularity (track spectrum density). The polygonal wheel can affect the track irregularity inspection data with its wavelength being less than or equal to wheel length and the former is more affected. It also affects the long-wave track inspection irregularity. The maximum influence wavelength is only related to wheel perimeter and sampling interval. The proposed dynamic detection model can effectively extract the amplitude of polygonal wheels from track irregularity inspection data. The measuring error can be limited to 0.02 mm.