荆楚理工学院学报2024，Vol.39Issue(2)：1-10,10.
基于BP神经网络算法的异步电机故障诊断系统研究
Research on Asynchronous Motor Fault Diagnosis System Based on BP Neural Network Algorithm
摘要
Abstract
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.关键词
故障诊断/MATLAB/BP神经网络/遗传算法/网络优化Key words
fault diagnosis/MATLAB/BP neural network/Genetic algorithm/network optimization分类
信息技术与安全科学引用本文复制引用
孙吴松..基于BP神经网络算法的异步电机故障诊断系统研究[J].荆楚理工学院学报,2024,39(2):1-10,10.基金项目
安徽省高校学科(专业)拔尖人才学术资助项目(gxbjZD2021111) （专业）
