A VMD and CNN Combined Fault Diagnosis Method for Rolling Bearings

Abstract

Aiming at the difficulty of extracting fault features of rolling bearings under the influence of strong background noise, a rolling bearing fault diagnosis method based on the fusion of variational mode decomposition (VMD) and convolutional neural network (CNN) is proposed. After decomposing the original variation signal into multiple components, the proposed method employs the Pearson correlation coefficient as the automatic decomposition termination threshold and the optimal modal component selection index; a convolutional neural network is constructed according to bearing fault features and the optimal modal component is used as the input to extract and classify the fault types. The experiments validate that the proposed method can accurately diagnose the rolling bearing faults, which is validated as a new method for rolling bearing fault diagnosis regarding strong background noise.

Keywords

• Rolling bearing;Fault diagnosis;Strong background noise;Variational mode decomposition;Convolutional neural network