Research on the application of BP neural wetwork in fault diagnosis of rolling bearings
The back propagation neural network(BPNN)is an important deep learning model,which has important applications and advantages in various fields.This article takes the bearing fault diagnosis as an example to mainly discuss the application of BP neural network.In this article,by using and optimizing BP neural network,the bearing fault data provided by Case Western Reserve University is processed by windowing and discrete Fourier transform,and then peak feature extraction is carried out.Then,the neural network model is learned and predicted using this data,and a network model that can accurately predict the bearing fault type is constructed.This model can improve the efficiency and accuracy of bearing fault diagnosis,and has important practical value.