Fault Diagnosis Of Rolling Bearing Research Based On Adaptive EEMD

As a commonly used mechanical component,rolling bearing has been used in mining,shipbuilding,railway,aviation,manufacturing and other fields.Rolling bearings are generally attached to rotating machinery,and rotating machinery is the key part of modern equipment,so its working state has a very important impact on social production efficiency,economic benefits and security.Once it breaks down,it may cause great losses to property and personal safety.Therefore,the research on fault diagnosis of rolling bearing is of great significance.In this paper,the ensemble empirical mode decomposition(EEMD)method is optimized on how to extract fault feature information better,and applied to rolling bearing fault diagnosis under constant and variable speed conditions under noise background.The specific research contents are as follows.(1)In order to solve the problem that the parameter selection of EEMD is difficult or mainly depends on human experience,an adaptive EEMD method based on adaptive weight particle swarm optimization(APSO)algorithm is proposed.In this method,the signal-to-noise ratio is used as an index to adaptively optimize the set average times and the auxiliary white noise intensity,which can deal with the problem of adaptive parameters when EEMD decomposes the bearing fault signal.The simulation and experimental results show that the speed and efficiency of parameter optimization is high,the IMF component decomposed by adaptive EEMD has high identification degree at fault feature frequency,and the method proposed can accurately extract bearing fault features.(2)Aiming at the poor performance of adaptive EEMD method in strong noise background,a fault feature extraction method based on stochastic resonance(SR)and adaptive EEMD is proposed.In order to improve the pre-processing effect of stochastic resonance,the stochastic resonance method of fractional power system is studied and analyzed;the fractional power system stochastic resonance and adaptive EEMD are combined to process three groups of bearing fault experimental signals under different work conditions,and then the method is compared with the adaptive EEMD method.The experimental results show that: compared with the adaptive EEMD method,the proposed method has higher signal-to-noise ratio and higher amplitude at fault characteristic frequency,which is suitable for rolling bearing fault diagnosis under strong noise background.(3)Aiming at the problem that it is difficult to extract the unknown fault features of rolling bearing under variable speed condition,a fault feature extraction method based on fractional Fourier transform(FRFT)and adaptive EEMD is proposed,which solve the problem that the adaptive EEMD method has poor effect in extracting bearing fault features under variable speed condition.Through simulation and experimental analysis,the effectiveness of the proposed method in extracting the characteristic signal of variable frequency bearing is verified.Compared with the resonance demodulation method,the experimental results show that the IMF component decomposed by the proposed method has stronger energy,fewer interference components and higher time-frequency resolution at the fault characteristic frequency,which has certain advantages over the resonance demodulation method.In conclusion,this paper studies weak fault feature extraction under different working conditions by improving EEMD method.The proposed method shows high accuracy,good fault extraction effect and certain feasibility,and has been verified in the rolling bearing fault diagnosis experiment.There are 63 pictures,4 tables and 96 references in this paper.