Fault Diagnosis of Rolling Bearing based on VMD Feature Energy Reconstruction and ADE-ELM

Aiming at the problem that the vibration signal characteristics of rolling bearings are not obvious and the diagnosis accuracy of traditional intelligent fault diagnosis algorithms is not high, a rolling bearing fault diagnosis method based on VMD feature energy reconstruction and ADE-ELM is proposed. Firstly, the VMD is used to process the bearing vibration signal to obtain the intrinsic mode function. Then, each IMF component is calculated, and the fault feature vector is reconstructed based on the feature energy ratio. Finally, the adaptive differential evolution (ADE) algorithm is introduced to optimize the hidden layer input weights and biases of the extreme learning machine. The simulation shows that the VMD feature energy reconstruction can accurately extract fault features, and the ADE-ELM model can effectively improve the fault classification accuracy. Compared with other methods, this method is more accurate and reliable.