Research on wheel-rail vertical load detection based on digital speckle pattern
Fast and effective detection of wheel/rail vertical load is of great significance for ensuring the reliability and safety of rail vehicles in service.Although existing wheel/rail load detection methods can effectively detect wheel/rail vertical loads,their measurement accuracy is limited by sensor deployment schemes and system calibration,and can only achieve fixed point detection.Therefore,this study introduced digital speckle image correlation technology to provide a new technical solution for rapid and non-fixed point detection of wheel/rail vertical loads.In order to achieve non-contact and rapid measurement of the stress field on the rail side,a sequence of rail speckle images under vertical load was synchronously collected by left and right cameras,and the sub-region pixel point matching and stress field calculation before and after deformation were carried out through image related theoretical models.In order to identify the wheel/rail vertical load using the rail side stress distribution obtained by digital speckle detection,a wheel/rail vertical load identification method based on the Extreme Learning Machine(ELM)was proposed.This method used Workbench finite element software to establish a numerical model of the steel rail.Through the numerical simulation results,areas that are relatively sensitive to stress changes were selected as the interest domain for wheel/rail vertical load detection.Based on the stress field dataset and corresponding load dataset,ELM network parameters were designed to achieve automatic recognition of wheel/rail vertical load.To verify the effectiveness of the proposed wheel/rail vertical load identification model,a digital speckle detection experimental platform for wheel/rail vertical load based on XT-DIC was established.The experimental results show that the ELM model constructed by inputting mode stress in the Y and Z directions has the best recognition performance,with a vertical load prediction error of only 5.357%.The research results provide a new and reliable way approach for non fixed point rapid detection of wheel/rail vertical load.
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