Research on Bearing Fault Diagnosis Based on Optimized CNN and Information Fusion

Aiming at the problem of the low recognition rate of a bearing fault under a single sensor condition, an intelligent detection method of a bearing fault based on an optimized convolutional neural network (CNN) and information fusion is proposed. Firstly, a NU216 bearing is selected as the research object to prefabricate fault defects. Then, the orthogonal test method is used to design the test scheme, and the vibration signal and acoustic emission signal of the NU216 bearing are collected; Secondly, the mutual feature fusion method of convolutional neural network is used to fuse the vibration signal and the acoustic emission signal. Then, the fused one-dimensional data is converted into a two-dimensional image dataset by continuous wavelet transform. Finally, the three datasets are divided into training sets and test sets, and input into the optimized convolution neural network model for training and testing. The test results show that the accuracy of fault diagnosis based on vibration signal is 95.76%, the accuracy of fault diagnosis based on acoustic emission signal is 92.33%, and the accuracy of fault diagnosis based on fusion signal is 98.59%.