Research on Incipient Fault Diagnosis of Rolling Bearing Based on Mode Selection
The vibration signals measured from a rolling bearing are complicated because of bearing rotation,component interac-tion and strong interference.It is difficult to extract fault features effectively for incipient fault because the features for incipient fault are small.The accuracy of fault diagnosis will be decreased.Therefore,a sensitive mode selection method was proposed to im-prove the accuracy of fault diagnosis.Firstly,Variational Mode Decomposition(VMD)algorithm was used to adaptively decom-pose the vibration signal and extract the local signal features.Secondly,based on the invariance of permutation entropy and its sen-sitivity to impulse signal,a sensitive mode selection method of fault factors was proposed to reconstruct the signal.The method could suppress the interference and noise in the vibration signals.Finally,comparison with other preprocessing methods using simulation and experiment has also been investigated.The simulation results show that the signal-to-noise ratio is improved by 60.5%and the root mean square error is reduced by 52.6%.The experimental results show the accuracy of fault diagnosis is in-creased by 24.3%.These results have confirmed that the fault factor method provides an effective method for fault diagnosis of roll-ing bearings.
Rolling BearingVMDMode SelectionPermutation EntropyFault FactorFault Diagnosis
万方数据
维普
