Research On Fault Diagnosis Of Rolling Bearing

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 and 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.And,the calculation of characteristic frequency in rolling bearing are introduced.Lastly,the experimental platform and signal acquisition instrument are introduced in detail.Secondly,aiming at the difficulty of selecting the filtering parameter L of the maximum correlation kurtosis deconvolution(MCKD)algorithm,this paper proposes a composite index method combining the kurtosis value and the fault characteristic frequency energy ratio to determine L.The processed signal is performed by the Teager operator demodulation,which can greatly increase the impact feature and accurately extract the fault features.Comparing the MCKD method before the improvement,the results show that the fault characteristics can be extracted more accurately by the filtering parameters selected by the proposed method.The fault feature energy demodulated by the Teager energy operator is much larger than the direct spectrum demodulation,and the fault characteristics are more obvious.Thirdly,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,the method of rolling bearing fault diagnosis under variable speed condition is proposed that combines improved variational mode decomposition(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,and the order spectrum is used to analysis corresponding components.The results show that the propose method can diagnose rolling bearing fault in variable speed.Finally,the traditional vibration signal analysis is mostly off-line processing,and there is a problem of large system size and poor real-time performance.An embedded fault diagnosis system based on ARM Cortex-M0 core embedded W7500 P chip is designed.The system uses the embedded hardware platform to integrate the signal acquisition,processing,and transmission into an embedded hardware platform for execution,enabling real-time online fault diagnosis.Finally,the classic resonance demodulation MATLAB algorithm program is transplanted into the embedded system for the extraction of bearing fault features.The experimental results show that the application of this method to this embedded system can quickly and effectively complete the fault feature extraction and achieve accurate diagnosis of fault diagnosis.