摘要
本文研究电机转子断条故障的诊断机理,选取小波包分析作为信号处理的前置手段,得到输出神经网络的特征向量。神经网络通过学习训练得到诊断结果,将小波的局部特性和神经网络的自学习特性结合起来,使诊断系统具有自适应分辨性和良好的容错性。本研究已经应用于电机故障诊断教学实验,针对特征向量提取方法实现了故障的诊断和区分。
This paper is concerned with fault diagnosis mechanism of broken rotor bars, choosing Wavelet Packet Analysis as the pre-positive means to get the eigenvectors of output neutral network. Neutral network, 'through learning and training, got the diagnosis results. By combining neural-network's self-study specific property with wavelets' partial specific property, it possessed self-suitable distinguish and good tolerance specific property. This research has been applied in teaching experiments of motor fault diagnosis, and feature extraction made fault diagnosis realized and the fault can be distinguished as well.
出处
《大电机技术》
北大核心
2013年第1期15-18,21,共5页
Large Electric Machine and Hydraulic Turbine