基于Elman神经网络的旋转机械故障诊断

Fault diagnosis for rotating machinery based on Elman neural network

提出了一种基于Elman神经网络的旋转机械故障诊断模型。该诊断模型综合了经验模态分解在故障特征提取和Elman网络在故障模式识别方面的优势,对故障信号进行经验模态分解,再对表征故障调制特征的本征模态函数计算瞬时幅值欧式范数构成特征矢量,将特征矢量输入到训练好的Elman神经网络中进行故障诊断。通过深沟球轴承故障诊断实例验证了所提故障诊断模型的有效性。

A novel fault diagnosis model for rotating machinery based on Elman neural network was proposed.By taking advantages of Empirical Mode Decomposition（EMD）in the fault feature extraction and the Elman neural network in fault pattern recognition,fault vibration signals were decomposed into several stationary Intrinsic Mode Functions（IMFs）.Then,the instantaneous amplitude L2-norms of the IMFs with modulation characteristics were computed and regarded as the input characteristic vector of the trained Elman neural network for fault diagnosis.Example of deep groove ball bearing fault diagnosis validated the effectiveness of the proposed fault diagnosis model.