基于小波包样本熵及GA-BP网络的旋转机械故障识别

Fault Diagnosis of Rotating Machinery Based on Wavelet Packet Sampling and GA-BP Network

针对旋转机械故障信号具有非线性、非平稳性特点,提出一种基于小波包样本熵及GA-BP网络的故障识别方法。首先对故障信号进行小波包分解,计算重构节点信号能量较大的前4个子频带振动信号的样本熵作为特征向量;然后将特征向量输入GA-BP网络模型进行故障类型识别,并且与传统BP网络作对比。实验结果表明:转子实验台不同故障信号的小波包样本熵不同,该方法对转子故障区别度更有效果,故障识别率明显提高。

Aiming at the non-linear and non-stable characteristics of the fault signal of rotating machinery, a fault identification method based on wavelet packet sample entropy and GA-BP network is presented. Firstly, using the wavelet packet to decompose the fault signal, then the reconstructed signals in the first four sub larger sample entropy energy generation were calculated as the eigenvector. Then the feature vector was input into the generic algorithm-back propagation (GA-BP) network model to identify the type of the fault, and at the same time comparing with the traditional BP network. The experimental results show that: The status of rotor bench is different, the wavelet packet sample entropy is also different, and this method is more effective for rotor fault discrimination, and the fault recognition rate is obviously improved.