Research On Fault Diagnosis Of Rolling Bearing Based On AR-MED And LMD

With the development of society and the continuous advancement of science and technology,Mechanical equipment is constantly developing in the direction of large-scale,Complex structure and increasingly sophisticated.Failure of mechanical equipment may cause serious economic losses and casualties.If the mechanical equipment failure is diagnosed in a timely manner and the serious consequences caused by the failure are effectively reduced,The enterprise will avoid serious economic losses and casualties.As an important part of mechanical equipment,Rolling bearings are called mechanical joints.People's mechanical equipment faults are mainly caused by rolling bearing faults.Therefore,It is important to study the fault diagnosis method of rolling bearings.The vibration signal generated by the mechanical equipment during operation contains a wealth of fault information,Analyze and process the collected vibration signals to obtain the status of the equipment operation,Judging whether the mechanical equipment and its components have failed.When the frequency analysis as mechanical fault feature extraction equipment commonly used analysis tool,Has been widely applied to fault feature extraction.In this paper,The local mean decomposition(LMD)is combined with autoregressive model(AR)and minimum entropy deconvolution(MED)to diagnose the rolling bearing fault.The main research content includes:Firstly,the traditional time-frequency analysis method is expounded,and the defects of the traditional time-frequency analysis method are pointed out.By comparing the simulation signals of the LMD method and the empirical mode decomposition(EMD)method,the results show that the LMD method is the reduction of iterations,the suppression of endpoint effects,and the residual margin after decomposition are significantly better than EMD,and the endpoint effects of the LMD method are improved.Secondly,for the early weak faults of rolling bearings,the fault characteristic information in the collected vibration signals is not obvious,and it is usually overwhelmed by noise and other intrinsic components,which affects the extraction of fault characteristic information.Therefore,the AR-MED pair vibration signal is proposed.The intrinsic components and noise are processed.The maximum kurtosis criterion is used to determine the optimal order of the AR model.The three main factors affecting the performance of MED are studied through simulation analysis.Finally,simulation analysis and case analysis are used to verify the proposed fault diagnosis based on AR-MED and LMD in rolling bearing fault diagnosis.Firstly,the effectiveness of the method is verified by analog signals.Then the experimental data collected by the bearing fault diagnosis equipment of the Electrical Engineering Laboratory of the Western Reserve University of America is used to analyze the example.The faults of the inner and outer ringsof the rolling bearing are diagnosed by the method proposed in this paper.The diagnosis results show the effectiveness of the proposed method.This method has certain reference value for fault diagnosis of rolling bearings.