Rolling Bearing Fault Diagnosis Based On IEWT And Adaptive FrFT Filtering

To ensure the normal operation of industrial production,it is necessary to ensure that every link is foolproof,and the condition monitoring of mechanical equipment is a very important link.Rolling bearings are widely used in mechanical equipment,which plays a vital role in all walks of life and many fields.However,due to its complex and changeable working environment,cracking,pitting and other problems are easy to occur in the daily operation of mechanical equipment,so that the mechanical equipment runs abnormally,errors occur,and the normal work task cannot be completed.This leads to unpredictable huge losses in the production process and a lot of inconvenience in life.In order to reduce the economic loss and inconvenience caused by bearing failure,it is important to discuss the rolling bearing fault diagnosis technology.The main research contents and achievements of this paper are as follows:(1)An improved empirical wavelet transform method is proposed,and the improved empirical wavelet transform is applied to fault diagnosis of rolling bearings.In this method,the maximum point of the small scale fine envelope is used as the dividing line,and the kurtosis merging rule is used to optimize the spectrum division,so as to minimize the influence of noise and so on,so as to obtain the best component.After processing and analyzing the bearing simulation and test data,the method presented in this paper performs well under the interference of strong noise,and can effectively extract the fault characteristics,and the denoising effect is remarkable,thus proving the effectiveness and advantages of the method.(2)An adaptive fractious-order Fourier transform filtering method is proposed.By iterating the peak seeking range and the peak seeking accuracy,the maximum peak point is approached quickly,and the optimal order and energy gathering center of the fault feature are obtained.The simulation signal analysis was established,and compared with the traditional two-dimensional ergodic fractional Fourier transform filtering method,the effectiveness and superiority of the adaptive fractional Fourier transform filtering method were verified.It can greatly shorten the running time and realize the adaptive filtering of variable speed fault signals on the premise of ensuring the accuracy.(3)A compound fault diagnosis method for rolling bearings based on improved empirical wavelet transform and adaptive fractional Fourier transform filtering was proposed.This method can effectively segment the frequency band of the signal,separate fault features from noise components,and process the original vibration signals with strong noise effectively.It greatly improves the signal-to-noise ratio of fault feature signals and makes up for the shortcomings of the adaptive fractional-order Fourier transform filtering method in anti-noise ability.The adaptive fractional-order Fourier transform filtering with the optimal modal component can further reduce the noise and extract the fault impact component,which can reduce the interference of noise to the greatest extent.By applying this composite method to analyze complex simulation signals and test information,it can effectively prove the effectiveness and advantages of this method,realize the complementary advantages of fault diagnosis methods,and improve the composite performance.