基于EMD和神经网络的车轮多边形识别方法研究

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关键词：车轮多边形；EMD；BP神经网络；能量；峭度系数；在线识别中图分类号： U270.1+1 文献标志码：B doi：10.20213/j.cnki.tdcl.2024.12.02.01

Abstract：To address the issue of non-stationarity in axle box vibration signals of rail transit vehicles，a diagnosis and identification method for wheel polygon order based on empirical mode decomposition （EMD） and BP neural network is proposed. Firstly，the EMD decomposition of the axle box vibration signal is carried out， and the signal is decomposed into the sum of several IMF components，then several IMF components containing main energy information are selected，the energy and kurtosis coefcient of each component are extracted，and the eigenvector is constructed，and then the eigenvector is used as the input of the neural network，and finally the neural network is trained to identify the wheel polygon order.The fault simulation test of whel polygon under different speeds of a certain type of EMU was carried out on the test bench，and two wheel polygon conditions of 17th order and 24th order were set. The test results indicate that the method based on EMD and neural networks can accurately and efectively identify the whel polygon order.Based on the simulation test on the test bench，an online identification database of wheel polygon order is established，which can identify and diagnose the wheel wear signal，predict the wheel state in advance，assist in detection and maintenance decision-making，and provide method reference and theoretical support for the safe operation and maintenance of rail transit vehicles.

Key words：wheel polygon；EMD;BP neural network；energy；kurtosis coeficient；online identification

随着列车向高速化与重载化快速发展，轮轨之间的相互作用力加剧，车轮更容易产生多边形磨耗。（剩余8174字）