基于EEMD多维特征的旋转机械故障识别方法研究

为有效诊断旋转机械故障，提出基于集合经验模态分解（EEMD）的多维特征提取故障诊断识别方法。利用EEMD将原始振动信号分解为若干个本征模态函数（IMF），分别计算原始信号和IMF分量的时域指标；将时域指标进行奇异值分解，得到奇异值特征向量，计算原始信号频率带能量比和IMF分量能量比；将IMF分量能量比、奇异值特征向量、频率带能量比组合为故障特征向量，作为神经网络的输入，对转子的工作状态进行诊断识别。结果表明：多维特征向量的识别效果优于EEMD能量特征，能更充分反映出转子的故障特征。

Research on Rotating Machinery Fault Recognition Method Based on EEMD Multi dimensional Features

In order to effectively diagnose rotating machinery faults, a fault diagnosis and recognition method for multi dimensional features extraction based on ensemble empirical mode decomposition (EEMD) was proposed. EEMD was used to decompose the original vibration signal into several intrinsic mode functions (IMF), the time domain index of the original signal and IMF component were separately calculated; the singular values decomposition for the time domain index were performed to obtain the singular value feature vectors, and the energy ratio of the original signal frequency band and the energy ratio of the IMF component were calculated; IMF component energy ratios, singular value feature vectors and frequency band energy ratios were used as fault feature vectors, which were used as the input to neural network to diagnose and identify the working state of the rotor. The results show that the multi dimensional feature vector recognition effect is better than of EEMD energy feature, which can more fully reflect the rotor fault features.