基于堆栈自动编码器的永磁电动机定子绕组故障诊断

Fault diagnosis of permanent magnet motor stator winding based on stacked auto encoder

目的:提高永磁电动机定子绕组故障诊断的准确率和全面性。方法:研究提出基于堆栈自动编码器(SAE)永磁电机定子绕组故障诊断模型,由SAE和Softmax分类器组成的神经网络,利用故障样本数据对该网络进行训练;利用模拟退火粒子群算法(SAPSO)对网络的连接权重和偏置进行寻优,确定其较优的网络结构。结果:利用该网络实现了永磁电动机定子绕组的匝间短路、相间短路以及相间绝缘降低和接线端子接触不良等故障诊断,其诊断准确率为99.40%,优于小波分析+Softmax、频谱分析+Softmax和SAE+Softmax 3种方法。结论:经过优化后的SAE+Softmax故障诊断模型鲁棒性好,受电机的转速和负载变化的影响小,可以提高永磁电动机定子绕组故障诊断的准确率。

Objective:To improve the accuracy and comprehensiveness of permanent magnet motor stator winding fault diagnosis.Methods:A fault diagnosis model of permanent magnet motor stator winding based on stack autoencoder(SAE)was proposed,and a neural network composed of SAE and Softmax classifier was used to train the network with fault sample data.The simulated annealing particle swarm optimization(SAPSO)algorithm was used to optimize the connection weight and bias of the network,and determined the optimal network structure.Results:The network had been used to realize the fault diagnosis of inter-turn short-circuit,inter-phase short-circuit,inter-phase insulation reduction,and poor contact of the terminals of the permanent magnet motor stator windings.Compared with wavelet analysis+Softmax,spectrum analysis+Softmax and SAE+Softmax,the diagnostic accuracy of this method was the highest,and the diagnostic rate was 99.40%.Conclusion:The optimized SAE+Softmax fault diagnosis model has good robustness and is less affected by motor speed and load changes,which can improve the accuracy of permanent magnet motor stator winding fault diagnosis.