基于声信号的VMD结合PSO-SVM车轮磨耗识别方法研究

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中图分类号：U279.2 文献标志码：A doi：10.3969/j.issn.1006-0316.2025.06.005

文章编号：1006-0316（2025）06-0031-09

Wheel Wear Recognition Method Combined VMD and PSO-SVM Based on Sound Signals

FENG Qianqian1，LIU Xingqi¹，LI Pengzhen1，XU Hairong¹，HAN Chungang1 SHI Zouliangl，LIU Yunhang²

（1. China Railway Urumqi Group Co.，Ltd.， Urumqi 830000， China; 2.State Key Laboratory of Rail n021 Chin.

Abstract ： On-line monitoring and identification of wheel polygon wear is one of the important problems to be solved in high-speed train operation and maintenance.A novel wheel wear identification method combined variational empirical mode decomposition （VMD） and particle swarm optimization support vector machine （PSO-SVM） based onsound signals is proposed in this paper.Firstly，the staticwheel polygon wear level is tested and the in-vehicle noisedataofhigh-speedtrain is collected.Secondly，the datarules ofinterior noise and wheel polygon wear amplitude are analyzed，and the relationship between interior noise and whel polygon is mapped.Thirdly， the PSO algorithm is applied to search the optimal decomposition parameters of VMD，and the redundant noise frequency band is filtered by band pass filtering. Then the time domain and frequency domain feature indexes are extracted.Finaly，thePSO algorithm is used to optimize the optimal model parameter combination of SVM，and the signal decomposition capability of VMD algorithm is effectively combined with the recognition capability of support vector machine.The experimental verification resultsshowed that the proposed wheel wear identification method could efectively identify the maximum wear amplitude of bogie wheels according to the noise signal inside the vehicle.The study provides guidance and help for wheel rotation and repair of high speed train.

Key words ∵ wheel polygon wear i support vector machine ; particle swarm optimization algorithm ; variational empirical mode decomposition

由于与轨道之间的相互作用，列车车轮在运行中会产生周向非均匀磨耗，进而导致出现车轮多边形磨损问题[1-2]。（剩余7827字）