Research On Fault Identification Method Of Rolling Bearing Based On Convolution Neural Network

As one of the most widely used mechanical parts,the health status of rolling bearing is closely related to the safe operation of the whole equipment.Finding the fault of rolling bearing timely and accurately can effectively avoid the loss caused by rolling bearing fault.Therefore,it is of great engineering significance to study the fault identification of rolling bearing.Taking the rolling bearing as the research object,this paper studies the fault identification of rolling bearing from different angles and different levels around the convolution neural network.This paper makes an in-depth research on the rolling bearing fault identification method in the aspects of convolution neural network optimization,integrated convolution neural network,compression integrated model parameters and data set imbalance.The main research contents are as follows:Firstly,the basic principle of convolution neural network is discussed,and it is applied to rolling bearing fault identification,which proves its effectiveness in rolling bearing fault identification,which lays a theoretical foundation for the paper.Then the optimization methods of different parts of convolution neural network are studied.on the basis of convolution neural network,it is improved from the aspects of data set,learning rate,loss function,transfer learning and so on,so as to promote its application in rolling bearing fault identification.In order to solve the problem that the training effect of neural network model is often limited by limited data sets and prediction problems,three integration methods are applied to convolution neural network to identify rolling bearings,and the relative performance of different integration methods is analyzed.and the best integration method and a variety of methods are selected to verify the effectiveness of this method.Finally,in order to solve the problem that it is difficult to deploy too many model parameters in practical engineering applications,a fault identification method based on particle swarm optimization knowledge distillation and convolution neural network is proposed and applied to rolling bearing fault identification.The results show that this method can compress the model parameters while ensuring high accuracy,and this method can also effectively solve the problem of data skew in practical application.