Fault diagnosis of rolling bearing based on TVD-VMD

When troubleshooting rolling bearings in industrial control equipment, the measured vibration signals are often complex and nonlinear and require noise reduction. In order to perform better fault feature extraction, a method based on a combination of Total Variation Denoising (TVD) and Variational Modal Decomposition (VMD), i.e., TVD-VMD, is used. Firstly, the total variation method is adopted for noise reduction of the signal. Then, the optimized variational modal decomposition is performed on the noise-reduced signal to achieve a more accurate extraction of the fault features. Finally, the method is applied to the actual measured signal. The results show that the method can effectively extract the fault features in complex vibration signals and achieve accurate fault diagnosis.