Study On A New Method Of Mileage Calculation And Correcting For Line Detector

The health status of the track is the basic prerequisite for ensuring the safe and stable operation of the train.At present,China's railways and urban rail transit use a combination of static inspection and dynamic inspection to check the track status.device.There are still a lot of deficiencies in the data processing of portable line detectors,among which mileage drift is one of the most common data quality problems in track dynamic detection data.In order to solve the problem of mileage drift,a new type of portable line detector based on inertial sensor components is used in this paper to study the mileage calculation and calibration of urban rail transit line detection data.First,for the noise of the detected data,the method of wavelet threshold denoising is used to denoise the data.A single variable analysis method was used to study the selection of optimal wavelet bases,threshold function selection,and threshold selection rules for the six detection items of triaxial acceleration and triaxial angular velocity.The denoising effect is evaluated by means of denoising evaluation indicators such as Root Mean Square Error,Signal to Noise Ratio,and Smoothness.Finally,the optimal wavelet base,threshold function,and threshold are selected for each item.Secondly,based on the de-noised data,the mileage calculation algorithm of the detection data is designed by the method of acceleration double integral calculation displacement,and the example analysis of mileage calculation error using Beijing Metro Line 1 data shows that the mileage error is very large.According to the characteristics of the stop of the subway train at the midway station in the detection process,based on the idea of inertial navigation error compensation,a mileage optimization calculation model suitable for urban rail transit was established,and the reliability and effectiveness of the model were verified by examples.Finally,in view of the fact that the mileage calculated by the mileage optimization algorithm still has some errors,according to the data characteristics of the course angular velocity of the train when passing the curve,a curve recognition model is designed to identify the start and end of the line curve in the detection data,thereby establishing a curve-based mileage calibration model.The mileage calibration model was verified using the detection data of Beijing Metro Line 1.The results show that the mileage calibration model can effectively reduce the mileage error.