基于小波和自适应模糊神经的旋转设备故障诊断

Fault Diagnosis of the Rotating Equipment Based on Wavelet Analysis and Adaptive Fuzzy Neural Network

针对传统的故障诊断方法和单独使用某一种智能诊断方法的局限性,将小波分析的故障特征提取方法和自适应模糊神经网络结合起来,对旋转设备的故障诊断进行了研究;通过对电机设备进行的故障诊断仿真实验,结果表明,与单独使用神经网路方法相比,该方法可以获得更高的故障诊断精度和诊断速度。

In view of limitation of the traditional fault diagnosis and the individual intelligent diagnosis, fault diagnosis of the rotating equipment is studied by combining fault feature extraction of wavelet analysis and adaptive fuzzy neural network. A simulation experiment of motor fault diagnosis shows that compared with the individual neural network, AN- FIS has higher diagnosis accuracy and speed.