Bearing fault diagnosis method based on complete center frequency distribution feature

Considering the difficulty of selecting sensitive fault features in bearing health diagnosis, a fault diagnosis method based on complete center frequency distribution feature (CCFDF) is proposed. By making full use of the sensitivity of the center frequency to the signal spectrum distribution in variational mode decomposition (VMD) and extracting the complete distribution feature of the center frequency under different parameter combinations, CCFDF can effectively characterize the difference in bearing vibration signals under different health conditions and avoid the parameter setting problem in VMD. Finally, two groups of experimental data are used to verify the effectiveness of the method, and the recognition accuracy was 99% and 97%, respectively. Therefore, this method can effectively characterize the difference characteristics of different signals and achieve the final bearing fault diagnosis.

Automated summary

Considering the difficulty in selecting sensitive fault features in bearing health diagnosis, a fault diagnosis method based on complete center frequency distribution feature (CCFDF) is proposed. By utilizing the sensitivity of the center frequency to the signal spectrum distribution in variational mode decomposition (VMD) and extracting the complete distribution feature of the center frequency under different parameter combinations, CCFDF can effectively characterize the difference in bearing vibration signals under different health conditions and overcome the parameter setting issue in VMD. Experimental results show that the method achieves high recognition accuracy of 99% and 97% for two groups of data, respectively, demonstrating the effectiveness of the proposed method in bearing fault diagnosis.