基于EEMD和SOM神经网络的水电机组故障诊断

Fault diagnosis of hydroelectric sets based on EEMD and SOM neural networks

针对水电机组振动信号的非平稳性和特殊性,提出一种基于集合经验模态分解(EEMD)的奇异谱熵和自组织特征映射网络(SOM)相结合的故障诊断方法。首先采用EEMD对振动信号进行分解,得到本征模态函数(IMF);随后进行奇异谱分解,得到反映振动信号的动态特征向量——奇异谱熵;最后将得到的特征向量输入经过训练的SOM神经网络中进行故障自动识别。结果表明:该方法可以准确地提取机组故障特征,具有更高的识别精度和更快的计算速度。

Aimed at the non-stationarity and particularity of the vibration signals of hydroelectric sets, a new fault diagnosis method combining singular spectrum entropy based on ensemble empirical mode decomposition（EEMD） with a self-organizing feature map network（SOM） is presented. First, EEMD was used to decompose the vibration signals of a hydroelectric unit to obtain their intrinsic mode function（IMF）, and then singular spectrum decomposition was performed to obtain their singular spectrum entropy： a dynamic eigenvector that characters the signals. Finally, this feature vector was input into a trained SOM neural network for automatic recognition of the fault. The results show that this method can extract the fault characteristics of the unit accurately and it has a higher recognition accuracy and faster calculation speed.