Fault diagnosis of automotive gearbox bearings based on MCKD with IPSO
In order to solve the problem of gearbox rolling bearing failure caused by frequent start-stop of hybrid electric vehicles in urban operation,the Maximum Correlation Kurtosis Deconvolution(MCKD)method was applied to the early fault diagnosis of gearbox bearing.In order to solve the problem that the filter order and shift number need to be selected manually in MCKD,a fault diagnosis method of maximum correlation kurtosis deconvolution with adaptive parameters was proposed.In this method,the maxi-mum correlation kurtosis in the signal envelope spectrum was taken as the objective function,and the filter coefficient L and shift number M were optimized by the Improved Particle Swarm Optimization(IPSO)algorithm.Finally,the bearing fault characteristics were extracted by the envelope spectrum.Simulation and experimental results show that this method can effectively reduce environmental interference,accurately extract fault features from strong noise,and realize fault diagnosis.
万方数据
维普
