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Research On Time-frequency Characteristics Of Ballastless Track Irregularity And Prediction Of Vehicle Vibration Of Simply Supported Beam Bridge

Posted on:2023-04-03Degree:MasterType:Thesis
Country:ChinaCandidate:L GongFull Text:PDF
GTID:2542307073488584Subject:Architecture and civil engineering
Abstract/Summary:PDF Full Text Request
The geometric irregularity of ballastless track in time domain and frequency domain has important influence on driving safety and ride comfort of high-speed train.In order to realize the comprehensive management of the time-frequency domain state of ballastless track irregularity,it is necessary to analyze the time-frequency characteristics of ballastless track irregularity by scientific methods.And the relationship between the time-frequency characteristics and vehicle body vibration needs to be established.The purpose is to explore the influence of time-frequency characteristics of ballastless track irregularity on the vibration of high-speed vehicles.As high-speed railway lines above the ground are mainly laid by bridges in China,and the simple supported beam bridge accounts for the largest proportion,so the ballastless track irregularity of simple supported beam bridge was taken as the research object in this paper.Based on the track irregularity detection results of a high-speed railway,and combined with the theory and method of signal processing and analysis,vehicle-track system dynamics and neural network,the time-frequency characteristics of ballastless track irregularity of simple supported beam bridge were analyzed.And the correlation between track irregularity and vehicle body vibration was studied.The VMD-LSTM prediction model of vehicle body vibration was put forward.And the relationship between track irregularity and vehicle body vibration was established.The main research work includes the following four aspects:(1)Based on the signal analysis theory,the variational mode decomposition method was introduced into the analysis of track irregularity signal.And the time-frequency characteristics of ballastless track irregularity VMD-HT analysis method was proposed combined with Hilbert transform method.The advantages of this analysis method were illustrated by comparing with EMD-HT signal time-frequency analysis method.Based on the detection results of ballastless track irregularity in simply supported beam bridge section,the main wavelength and energy distribution characteristics of track irregularity signals were analyzed by the established VMD-HT method,and this method was applied.(2)The power spectral density function of track irregularity signal was estimated by Welch method based on the test results of ballastless track and vehicle body response in simply supported beam bridge section.The coherence function curve of track irregularity and vehicle body vibration acceleration was obtained based on the coherence analysis theory.The correlation between track irregularity and vehicle body vibration acceleration in frequency domain was studied and determined.(3)Based on the vehicle-track system dynamics theory,the simulation analysis model of high-speed vehicle vibration was established by using UM software,and the reliability of the model was verified from the nonlinear critical speed analysis and vehicle body vibration simulation analysis of the simulation model.(4)Using the established high-speed vehicle vibration simulation analysis model,the transmission relationship between the irregularity wavelength and the vehicle body vibration frequency was ascertained,and then the vehicle body vibration VMD-LSTM prediction model based on track irregularity was established.The application analysis of this prediction model was carried out combined with case analysis,and it was compared with LSTM,VMD-RNN and VMD-CNN prediction models.The results show that the VMD-LSTM model has better prediction performance.
Keywords/Search Tags:time-frequency characteristics, vibration prediction of vehicle body, ballastless track irregularity, simple supported beam bridge, variational mode decomposition, deep learning neural network
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