基于模糊神经网络的故障诊断在永磁直线同步电动机中的应用

Fault diagnosis based on fuzzy neural network and its application in permanent magnet linear synchronous motor

针对永磁直线同步电机的特点,应用基于模糊神经网络的故障诊断方法,将模糊理论与神经网络融合在一起,实现了对该电机故障的模糊诊断。介绍了模糊神经网络的结构和学习方法,利用了一种阈值向量故障判别方法,使永磁直线同步电动机的故障判别灵活、可靠。仿真结果表明,该方法是行之有效的。

A fault diagnosis approach based on a fuzzy neural network is applied to the permanent magnet linear synchronous motor (PMLSM). The structure of the fuzzy neural network is given, and the learning method is introduced.A fault discriminate method for threshold vector is utilized, which makes fault discrimination of the PMLSM flexible and credible. The simulation shows that it is useful and effective.