基于卷积神经网络的直线同步电动机电枢绕组故障诊断

Fault Diagnosis of Armature Winding of Linear Synchronous Motor Based on Convolutional Neural Network

通过绕组函数理论对直线同步电动机进行分析,提出一种基于卷积神经网络(CNN)的直线同步电动机故障诊断方法。从直线同步电动机的数学模型出发,基于绕组函数理论对电动机正常状态和匝间短路故障状态进行仿真,对电流波形图进行快速傅里叶变换(FFT)得到不同状态的数据集。利用CNN中的GoogLeNet网络结构,在保持网络空间维度的同时不增加故障诊断的计算量。将数据集输入到网络模型进行故障诊断,仿真结果表明GoogLeNet网络结构对直线同步电动机电枢绕组的短路故障识别率达到了96.5%以上。

The linear synchronous motor is analyzed through the winding function theory,and a fault diagnosis method of linear synchronous motor based on convolutional neural network(CNN)is proposed.Starting from the mathematical model of the linear synchronous motor,the normal state and turn-to-turn fault state of the motor are analyzed based on the winding function theory.The short-circuit fault state is simulated,and the fast Fourier transform(FFT)of the current waveform is performed to obtain data sets of different states.This method uses the GoogLeNet network structure in CNN to achieve the characteristics of not increasing the amount of calculation while maintaining the dimension of the network space.The data sets are then input into the network model for fault diagnosis.Simulation results show that the GoogLeNet network structure reaches over 96.5%recognition rate for the short-circuit fault of the armature winding of linear synchronous motor.