Rolling Bearing Fault Diagnosis Based on Improved FMD and SK
Considering that the fault signals of rolling bearings usually exhibit non-stationary characteristics,are easily disturbed and cannot be effectively diagnosed,an improved feature mode decomposition(FMD)combined with spectral kurtosis(SK)is proposed to extract fault features of rolling bearings.First,we studyed the filtering characteristics of FMD to select two important input parameters reasonably;then,we used FMD to process the rolling bearing fault signal to obtain the decomposition results of multiple modal components,and calculated the relationship between each component and the original signal by the gray relational degree and mutual information to select the best modal component;finally,we combined with spectral kurtosis,bandpass filtering is performed on the best modal component to emphasize the impact component within the signal,thereby enabling extraction of the characteristic fault frequency.By analyzing the simulation signal and experimental data,It is found that the proposed method can effectively extract the peak envelope spectrum and harmonics of the fault signal characteristic frequency,and the spectral line features are more obvious compared to other methods,indicating that using this method to diagnose faults in rolling bearings is feasible.
万方数据
维普
