Study On Axle Acoustic Emission Feature Extraction Methods Based On Local Mean Decomposition

With the rapid development of high-speed railway enterprise in our country,the railway vehicles has also become an important means of transport in our country.The axle is one of the major parts of the bogie of railway vehicle and is also the core component of the wheel,which alsways bears the wear,crack and fracture.Therefore,we can take advange of the acoustic emission signal technology to diagnose the fault of the axle and find the fault in time.If the problems can not be dealt with well,which will bring bad influence on personal safety and economy.Therefore,it is of great practical significance to study the safe of the railway vehicle and its fault diagnosis.For the study of this paper,the experiment of the railway vehicle axle fatigue crack acoustic emission is carried out,the acoustic emission signal of the axle are collected in the experiment.In fact,the acquisition of signals contain a lot of noise,which will seriously affect the signal decomposition and subsequent extractions of fault feature.Therefore,it is necessary to utilize the relevant method to denoise the signal noise.In this paper,the auto-correlation denosing method will be used to reduce the noise,and the feasibility of the method is confirmed by the simulation signal,and at the same time,the method will be used to reduce the noise of the experimental datas.Then the LMD and EMD method are used to process the simulation signal,and the results show that the LMD method has its own advantage over the EMD method in the inhibition of endpoint effect;taking into account of the advantages of adaptive signal decomposition,which LMD algorithm has,the signals after de-noising are decomposed to obtain a series of product function components(PFs)by LMD.At the same time,the software of MATLAB is used to write the program,and in the paper,the kurtosis,sample entropy and Lempel-Ziv complexity algorithm are used to extract the fault feature from the PF components(the three methods are combined with LMD method)in order to distinguish the fault states.The experimental results show that the sample entropy of the axle running in the state of slight crack states is greater than the corresponding value moderate and severe crack states,but the kurtosis and Lempel-Ziv complexity index are less than the other two fault conditions.In summary,the method applied in the paper,including autocorrelation de-noising as well as kurtosis,sample entropy and Lempel-Ziv complexity extracted from the product function components after LMD,can extract fault characteristics and diagnosis faults very well.