Rolling Bearing Fault Diagnosis Based On Variational Mode Decomposition

The Rolling bearing is an important components in the train bogie system of rotating machinery,So its health working is important foundation for train's driving safety,Thus it is extremely significant to research fault diagnosis of train rolling bearing.In this paper,The train rolling bearing is taken as the research object,through to analysis bearing vibration signal,Aiming at the issue of the bearing fault mechanism,vibration signal characteristics,fault feature frequency extraction method,a series of research work is conducted.The main research contents are listed as follows:Firstly,the background and studying meaning of the project are elaborated systematically on the basis of industrial application.Then a more comprehensive description of the rolling bearing is provided,which contains the current situation,vibration signal analysis method and bearing failure type.Lastly,the calculation of characteristic frequency in rolling bearing are introduced.Secondly,Aiming at the problems that the Rolling bearing fault signal usually present multi-component modulation and low-SNR characteristics.the fault diagnosis method based on the VMD and autocorrelation analysis is proposed.At first,through to the autocorrelation analysis eliminate the noise in fault signal and highlight periodic signal constituents.Then variational mode decomposition(VMD)is used to decompose the denoised signal into a number of intrinsic mode functions(IMF),the IMFs of kurtosis was selected and demodulated with Teager energy operator,At last the bearing fault type is distinguished through the energy spectrum.Thirdly,a new time-frequency analysis method called frequency slice wavelet transform(FSWT)is able to analyze fault signal time domain and frequency information.In order to solve the problems that fault feature is difficult to extract for FSWT under intensive noise background,A method of VMD combine singular value decomposition(SVD)joint de-noise is apply to fault signal preprocessing.At first,VMD is used to decompose the fault signal into a series of IMF,The IMFs containing fault information are reconstructed based on kurtosis-correlation coefficients criterion,then the SVD is used to reduce noise again and increase the signal-noise ratio,finally the FSWT used to the de-noise signal highlights the time-frequency distribution and extracts fault feature.The results show that this method can eliminate noise effects effectively and precisely extract the fault feature frequency of time domain and frequency domain.Finally,The rolling bearing vibration signals in the rotating speed-varying condition is non-stationary and its frequently is modified by rotating frequently.When the spectral analysis method is used to this kind of signals,it will present serious “frequency ambiguity”.In order to solve the problems,A new method of rolling bearing fault diagnosis under variable speed condition is proposed that combines improved VMD with order tracking analysis.Firstly,Using order tracking sampling transform vibration signals in time domain into angle domain,then the angle domain signals are decomposed into several IMFs by improved VMD,The order spectrum is used to analysis corresponding components,The result show that the propose method can diagnose rolling bearing fault in variable speed.