Intelligent Fault Diagnosis of Bearing Using Multiwavelet Perception Kernel Convolutional Neural Network

Strong background noise characteristics of vibration signals cause issues with poor identification capability of features by fault diagnostic models. To address this issue, a method is proposed for intelligent fault diagnosis of bearing using multiwavelet perception kernel (MPK) and feature attention convolutional neural network (FA-CNN). First, four MPKs are constructed to decompose the vibration signals in full-band multilevel. Second, improved multiwavelet information entropy (IMIE) of the frequency band components is calculated. The calculated component entropies of the corresponding frequency bands are integrated to construct frequency band clusters (FBCs) from low to high frequencies. Third, joint approximate diagonalization of eigenmatrices (JADE) is introduced to perform feature fusion for every FBC to eliminate redundant information, and fused features from low to high frequencies are obtained as original inputs. The FA-CNN bearing fault diagnosis framework is constructed for intelligent fault diagnosis of bearings. Finally, the effectiveness of the proposed method is verified by two cases. The results show that the proposed method has high fault feature recognition capability.