Research On Rolling Element Fault Diagnosis Method Based On Variational Mode Decomposition

Rolling element bearings usually work under complicated working environments and the longterm effects of alternating loads can frequently lead to bearing failure.As one of the most vulnerable parts to fail in mechanical equipment,it is necessary to carry out the research on the running state monitoring of rolling bearing.Most of the existing bearing monitoring and diagnosis methods pay little attention on variational speed condition.Aiming at the above problems,this article studies the adaptive processing and analysis method of the rolling bearing vibration signals and proposes fault diagnosis methods of rolling bearings based on variational mode decomposition.2In this article,the basic theory of envelope demodulation is introduced at first,which lays a foundation for adaptive frequency band segmentation and selection of signals in the following article.And the theoretical basis and calculation flow of variational mode decomposition and variational nonlinear frequency modulation mode decomposition are described.In order to adaptively determine the parameters of variational mode decomposition and reduce the dependence on prior knowledge in signal processing,a parameter optimization method based on Deferential Search(DS)is proposed in this article.The DS algorithm is used to optimize the parameter combination of the VMD,after which the vibration signal is decomposed to obtain the intrinsic mode function.Then the correlation kurtosis of each IMF is calculated and used to reconstruct the vibration signal.Finally,the envelope spectrum analysis of the reconstructed signal is performed to extract bearing fault features.The experimental data analysis shows that the bearing fault feature adaptive extraction based on DS-VMD reduces the dependence on the prior knowledge acquisition of the fault,and can effectively extract the key parameters of the variational modal decomposition Due to the disadvantages of traditional methods,a new diagnosis method for denoising signals,known as the VMD-Scale Space Based Hoyergram is proposed in this article.The spectrum of the vibration signal is smoothed by means of scale space theory,and the local minimum between each two center frequencies calculated by parameter optimized VMD is determined as the boundary.Thirdly,the specific filter center frequency and filter bandwidth are obtained via Hoyergram,in which the kurtosis index of the fast kurtogram is replaced by the Hoyer index,and the spectrum is segmented based on its distribution.The frequency band with the largest Hoyer index value contains the impact information.Finally,the periodicity impact can be observed in the corresponding envelope spectrum.In the light of the difficulty of rolling element bearing defect diagnosis under variable speed while using traditional spectrum analysis,a new method based on Fast Hoyergram and improved variational nonlinear chirp mode decomposition is proposed.First,Fast Hoyergram is used to determine the resonance frequency band where the bearing fault impact locates.Then bandpass filtering is utilized to extract the component of rolling bearing vibration signal and the result is mixed with the signal after lowpass filtering.Secondly,the ridges of rotating frequency and bearing fault frequency are extracted based on multi-component collaborative speed estimation,and used as the input parameter of VNCMD to extract the rotation and bearing fault impact components.Finally,the type of rolling bearing fault can be determined by characteristic frequency ratio.Simulation and experimental signal analysis prove the effectiveness of the proposed method which can obtain accurate time-frequency ridges and correct characteristic components.