In order to realize the diagnosis of inter-turn short circuit faults in traction motor stator winding,an intelligent fault diagnosis method was proposed based on one-dimensional convolutional neural network(1D-CNN).Firstly,a dis-crete three-layer wavelet transform was applied to the motor stator currents in both healthy status of the motor and the ca-ses of inter-turn short circuit faults in different phases and at different fault severity levels using Daubechies-8 wavelet,to obtain high-frequency and low-frequency wavelet decomposition coefficients.Next,the L2 norm of the coefficients was calculated to represent the features of the traction motor currents.Lastly,the 1D-CNN model was designed,trained,and used as a classifier to achieve"end-to-end"intelligent fault diagnosis of inter-turn short circuit in the traction motor sta-tor winding.A test platform was designed and built for the diagnosis of inter-turn short circuit faults in induction motor stator winding.The results demonstrate that the method can accurately and effectively diagnose minor inter-turn short cir-cuit faults in the stator winding.Under closed-loop control,in the case of one-turn short-circuit fault in the motor,the diagnostic accuracy reaches 90.5％,with the fault phase being effectively distinguished.
关键词
牵引电机/匝间短路/故障诊断/小波分解/卷积神经网络
Key words
traction motor/inter-turn short circuit/fault diagnosis/wavelet decomposition/convolutional neural network
维普
万方数据