Data Processing For Precise Measurement And Adjustment Of Ballastless Track For High-speed Railwa

With the increasing operational mileage and running speed,the inspection and maintenance of high-speed railroad ballastless tracks have put higher requirements.China adopts a combination of dynamic and static assessment to achieve accurate monitoring and precise measurement to control the way.Generally,through the track inspection,dynamic vehicle inspection overall monitors the geometric state of the track to determine the section of over-limit;then,the use of static measurement results to guide the fine-tuning operations.Traditional static detection,although the detection accuracy can still meet the requirements,is relatively backward and inefficient,and gradually cannot adapt to the development requirements of high-speed railways.Therefore,developing portable track inspection trolley research is of high practicality.Among them,the critical technology is the data processing method of precise measurement and precise adjustment of the track inspection trolley.The research on it is significant for track measurement and adjustment operations.The acceptable measurement and fine-tuning data processing proposed in this paper contain two parts: track midline deviation data processing and track fine-tuning data processing.Firstly,the prism coordinate data obtained from the dynamic measurement of the track inspection trolley,the geometric parameters of the inspection trolley structure,and the measurement data of the gauge and inclination sensors are used to solve the track median coordinates and compare with the design parameters to obtain the original deviation data of the track median.Then,wavelet decomposition and segmental spline interpolation are used to preprocess the original data and receive the deviation value of the track centerline at the rail sleeper.Finally,the correction function is constructed by the dynamic and static detection results of the near and far stations of the track inspection trolley to correct the original data after preprocessing and get the actual detection data.Firstly,the trend line is obtained by polynomial fitting,and the problematic section is screened out by using it as a benchmark.Then,the finetuning model of the complex area is established and solved by the IGA-PSO algorithm to derive the track median fine-tuning scheme.After that,the level and gauge deviation are combined to obtain the acceptable tuning scheme for the left and right tracks.Finally,the approximate smoothness calculation formula compares the smoothness quantification of the data before and after the fine-tuning to demonstrate that fine-tuning data processing can restore track smoothness.Through site experiments and multiple experiments on a section of high-speed rail track under construction.After data processing,the deviation between the dynamic detection results of the track inspection trolley and the static detection results is within 0.25 mm and can save 2/3of the inspection time,which verifies the accuracy and applicability of the track fine measurement data processing method and improves the efficiency of fine measurement.In the fine tuning data processing,adjusting the overrun threshold under the premise of ensuring the smoothness requirement can reduce the overall adjustment amount and save the material and time cost.