Establishing health indicators of urban rail transit train wheelsets based on tensor breakdown
Throughout the operation of metro vehicles,the ongoing wear of the wheel tread and rim against the track results in the degradation of wheel performance.Recognizing and evaluating various states of wheel degradation is a crucial aspect of predicting vehicle faults and managing the health of vehicles.In recent years,intelligent identification of wheel degradation states in metro systems,based on vibration signals and machine learning,has played a significant role in reducing manual dependence and optimizing reprofiling strategies.However,in the actual operation process,metro wheelsets are typically influenced by factors such as load and rail conditions.The vibration signal is susceptible to substantial noise interference,leading to an inconspicuous degradation trend,which makes it difficult to directly identify the changes in state.To address this challenge,this article suggests a method for constructing a health indicator based on tensor reconstruction.This method involves firstly obtaining the core tensor of the original signal using tensor tucker breakdown,which is based on the advantage that tensor breakdown can effectively excavate the high-dimensional essential information of the signal.Secondly,the tensor reconstruction and Savitzky-Golay filtering methods are used to reduce the noise of the signal.Finally,based on this,a deep autoencoder network is used to extract the deep degradation features,and to establish the health indicators of the wheel degradation process.The experimental results using wheel vibration signal data from a Beijing metro line demonstrate that the health index constructed through this method exhibits a favorable trend and monotonicity.It accurately describes the whole wheelset degradation process,and the abnormal warning position obtained corresponds accurately to the change in wheel diameter wear and the actual reprofiling records.This proposed method offers an intelligent solution for the health management of metro wheelsets,proving to be of significant practical value.
NETL
NSTL
万方数据
维普
