基于流行-IMF奇异值熵的转子故障特征提取方法

针对转子振动信号的非平稳性以及微弱故障特征难以提取的问题，提出一种基于集合经验模式分解(ensemble empirical mode decomposition，简称EEMD)的奇异值熵和流形学习算法相结合的故障特征提取方法。首先，对原始振动信号进行EEMD分解，得到若干本征模态函数(intrinsic mode function，简称IMF)分量，根据峭度 欧式距离评价指标选取故障信息丰富的敏感分量，组成初始特征向量，求其奇异值熵；其次，利用近邻概率距离拉普拉斯特征映射算法（nearby probability distance Laplacian eigenmap,简称NPDLE）对奇异值熵组成的特征矩阵进行降维处理；最后，将得到的低维特征子集输入到K-近邻(K-nearest neighbor，简称KNN)中进行模式辨识。用一个双跨度转子实验台数据集和Iris仿真数据集对所提方法进行了验证，结果表明，IMF奇异值熵和NPDLE相结合的方法可以有效地实现转子故障特征提取，提高了故障辨识的准确性。

Fault Feature Extraction Method For Rotor Fusion of IMF Singular Value Entropy and Improved LE

Aiming at the problem that the rotor vibration signal is non-stationary and the weak fault features are difficult to extract, a fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value entropy and manifold learning algorithm is proposed. Firstly, the original vibration signal is decomposed by EEMD, and some intrinsic mode function (IMF) components are obtained. According to the kurtosis-European distance evaluation index, the sensitive components with rich fault information are selected to form the initial eigenvector, and the singularity is obtained. Value entropy. Then, using the near probability distance Laplacian eigenmap (NPDLE), the feature matrix composed of singular value entropy is reduced. Finally, the obtained low-dimensional feature subset is input into the K-nearest neighbor (KNN) for fault pattern recognition. The proposed method was validated by a two-span rotor test bench dataset and Iris simulation dataset. The experimental results show that the combination of IMF singular value entropy and NPDLE can effectively extract the rotor fault features and improve the accuracy of fault identification.