基于神经网络的旋转机械振动故障诊断

Fault Diagnosis of Rotating Machinery Vibration Based on Neural Network

介绍了一种通过神经网络算法进行故障诊断的方法,神经网络的输入为通过对转子模拟信号进行傅里叶变换得到的典型频谱特征,将旋转机械不对中、不平衡、碰摩、涡动四种典型的故障作为网络的输出。首先用传统的BP网络算法进行诊断,得到故障诊断的精度,再将模糊理论与神经网络相结合,取长补短,组成模糊神经网络,对故障进行识别,从而得出模糊神经网络在模式识别方面具有更大的优越性的结论。

This paper describes a neural network approach for fault diagnosis,which make typical spectral characteristics as the neural network input,the typical spectral characteristics can be got from fourier transform,and the network output is four rotating mechanical failure-the misalignment fault,unbalance fault,rubbing fault and eddy fault.Firstly,the paper uses the traditional BP network algorithm for diagnosis,and gets the accuracy of fault diagnosis,then combines the fuzzy theory and neural networks,identifies the fault with the fuzzy neural network,so as to get the conclusion that fuzzy neural network has more advantages in pattern recognition.