Research on Asynchronous Motor Fault Diagnosis System Based on BP Neural Network Algorithm
In order to ensure the safe and reliable operation of the motor,the BP neural network algorithm is studied for fault diagnosis of asynchronous motor.Through the MATLAB platform,two gradient descent methods of additional momentum factor and adaptive learning rate are used for network training,and the BP network model for fault diagnosis is built.The MSE value is used as the index to optimize the number of nodes,momentum factor and learning rate of the best hidden layer,and the genetic algorithm is used to optimize the initial weight of the BP network,and the fault test samples are simulated.The results show that the MSE value of GA-BP network model is lower than that of MF-BP and AG-BP,which is only 0.009163.The optimized diagnosis prediction re-sult is almost the same as the target value.The improved fault diagnosis system model based on genetic algorithm can meet the ap-plication requirements of asynchronous motor fault diagnosis.
国家科技期刊平台
NSTL
万方数据
维普
