Application of Wavelet Analysis and Intelligent Computing in Bearing Fault Diagnosis

The quality of rolling bearing has a great impact on mechanical equipment. In order to diagnose the rolling bearings faults, the application of wavelet analysis combined with Hilbert-Huang transform in rolling bearing fault diagnosis is proposed. Firstly, the information of rolling bearing is obtained by sensors, and the primary feature information of the original vibration signal is extracted by the Fast Fourier transform. Then, the random noise in the vibration signal is removed by wavelet transform. Finally, the Hilbert-Huang transform is applied to the noise-reduced vibration signal to extract the fault features and complete the fault diagnosis. The above steps are realized with MATLAB, and then the bearing data is imported to realize fault diagnosis. Through the experiment, it can be found that this method can reduce the noise, greatly retain the fault information, and better complete the bearing fault diagnosis.