VMD 及 PSO 优化 SVM 的行星齿轮箱故障诊断

以故障高发的行星齿轮传动系统为对象，提出基于变分模态分解(variational mode decomposition, VMD)及粒子群算法(particle swarm optimization, PSO)优化支持向量机(support vector machine, SVM)的故障诊断方法。首先，对信号进行VMD分解，采用改进小波降噪的方法处理分解后的本征模态分量(IMF),并对处理后的分量进行重构，凸显信号蕴含的信息；然后，对处理后的振动信号进行特征提取，分别提取信号的样本熵和均方根误差，并组成输入矩阵；最后，引入PSO优化SVM的关键参数，将提取的特征向量输入PSO-SVM进行训练和识别。将该方法应用于行星传动试验平台获取的行星轮裂纹故障、太阳轮轮齿故障及行星轮轴承故障信号，通过多维比较，验证了该方法的有效性。

Fault diagnosis method of planetary gear box based on variational modal decomposition and particle swarm optimization support vector machine

This paper takes the planetary gear transmission system with high incidence of faults as the object, a fault diagnosis method based on variational mode decomposition (VMD) and particle swarm optimization (PSO) to optimize support vector machine (SVM) is presented. Firstly, the signal is decomposed by VMD, the decomposed components are processed by improved wavelet method, and the processed components are reconstructed to highlight the signal. The weak information of SVM is extracted. Then, the sample entropy and root mean square error of the processed vibration signal are extracted, and the input matrix is formed. Finally, PSO is introduced to optimize the key parameters of SVM, and the extracted eigenvectors are input into PSO-SVM for training and recognition. The method is applied to the planetary gear crack fault, the solar gear tooth fault and the planetary gear bearing fault signal obtained by the planetary transmission test platform. The effectiveness of the method is verified by multi-dimensional comparison.