Research On The Identification Of Crack Status Through Axle Acoustic Emission Signal Based On LMD And Grey Correlation Analysis

With the development of China's economy,the Chinese people's demand for quality of life is getting higher and higher,which is manifested in all aspects of food and clothing.With the acceleration of urbanization,more and more people begin to focus,increasing the traffic pressure between the city and the city.In order to solve this problem,China has begun to develop rail transportation.The construction of rail vehicles,especially high-speed trains,is complex.And the railway lines are distributed all over the country,and the operating environment is very different.Each train operates in a very demanding environment.The safety of high-speed trains is higher.As the core of the track vehicle,the axle carries all the weight from the car body and the vehicle.The importance of axle safety is self-evident.Due to the bad nature of the operating environment,the axle part is prone to failure.Once the axle is broken in operation,it will bring immeasurable loss to the people's property and life safety.Therefore,it is necessary for us to make fault diagnosis and risk assessment on axle.However,due to the complexity of vehicle construction,axle failure is not easy to be found,and many of them rely on backward methods.We need to find the proper method to diagnose and analyze the axle.Based on the research in this paper,the fatigue test of train axle is designed,and the acoustic emission signal of axle crack is obtained.The data state is divided into three stages according to the time sequence,which are defined as initial crack stage,middle crack stage and late crack stage.The main innovation point is the combination of LMD and grey correlation analysis,and a new weighting method based on event importance is proposed.Firstly,the non-stationary signal is decomposed into a pure frequency modulation signal and an envelope signal by LMD decomposition of the acoustic emission signal.The signal is decomposed to the cut-off condition to obtain the component signal containing the global multiple frequencies.The time domain signal of PF component is transformed into frequency domain signal by Fourier transform.The amplitude spectrum of PF component signal iscalculated,the mean square root of amplitude spectrum is calculated,and the characteristic factor of each state is formed,which is used as the characteristic parameter of the standard sequence of correlation analysis.Based on the fact that the effect of feature factors on the event is different in the grey relational analysis,a weight calculation method based on event importance is proposed to improve the grey correlation analysis model.The accuracy of the improved grey relational degree model and the traditional grey relational degree model is verified by using the data of the known crack state,and the accuracy of the risk assessment is calculated.The results show that the accuracy of weighted grey correlation degree is higher than that of traditional grey correlation degree,and the axle crack under different working conditions is used.The method proposed in this paper is verified by the grain data,the applicability of the proposed model is verified by calculating the crack data under different working conditions,and the evaluation effect between the weighted grey correlation degree and the traditional grey correlation degree is compared.The effect of artificial selection resolution coefficient on the result of risk identification is discussed.Through the curve fitting of the correlation degree,the process of crack initiation and propagation is described,which provides a reference for better understanding the variation law of crack.