Bearing Fault Diagnosis Based on PSO-BP Neural Network
PSO algorithm is applied to optimize the weight and threshold of BP neural network and conduct the fault diagnosis of rolling.The acceleration data of driving end and the acceleration data of fan end are taken as input to ouput three different states of bearing by training network,so as to realize the fault diagnosis of bearing.The simulation results show that the network model can accurately identify the running state and fault type of bearings,and the test accuracy of normal samples reaches 98％.Compared with BP neural network,the test accuracy is greatly improve with stronger generalization ability and higher feasibility.
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