多维度能量熵提取的不同负载下电机轴承故障诊断

为了提高电机轴承故障诊断的准确率，针对电机轴承故障不稳定的振动信号及故障特征提取困难问题，提出了一种基于变分模态分解（VMD）能量熵与卷积神经网络（CNN）相结合的电机轴承故障诊断方法。为了使故障的特征更精确地体现出来，采取三维度的能量熵提取办法，将轴承故障分为内圈磨损、外圈磨损和保持架断裂三类，然后每个类别再细分为负载为0%、25%和50%三种情况，共9种情况。利用VMD方法将故障信号分解得到内禀模态函数(IMF)的分量并提取各个维度IMF的能量熵值从而构成特征向量。结果表明该方法可以有效提高故障诊断正确率。

Fault Diagnosis of Motor Bearings with Different Loads Based on Multi-Dimensional Energy Entropy Extraction

In order to improve the accuracy of motor bearing fault diagnosis, and aiming at the problem of unstable vibration signals and the difficulty in extracting fault feature of motor bearing fault, a motor bearing fault diagnosis method based on the combination of variational mode decomposition (VMD) energy entropy and convolutional neural network (CNN) is proposed. In order to reflect the characteristics of faults more accurately, a three-dimensional energy entropy extraction method is adopted to divide the bearing faults into three categories, namely, inner ring wear, outer ring wear and cage fracture. Then each category is subdirided into three cases with loads of 0%, 25% and 50%, for a total of 9 cases. Firstly, the VMD method is used to decompose the fault signal into components of the intrinsic mode function (IMF) and the energy entropy of each dimension IMF is extracted to form the feature vector. The results show that the method can effectively improve the accuracy of fault diagnosis.