基于CEEMDAN样本熵与PNN的行星齿轮故障诊断

为对行星齿轮进行故障诊断，采用自适应噪声完备总体经验模态分解（CEEMDAN）方法对采集的信号进行分解。对分解得到的各IMF分量进行相关系数计算，优选出与原始信号相关性较大的前4阶分量进行样本熵计算，得到特征值，构成特征向量。将特征向量输入到概率神经网络系统中进行诊断，且与基于局域均值分解的样本熵特征提取方法的诊断结果进行对比。结果表明：利用CEEMDAN样本熵提取的特征值能更精准地反映系统的故障特性，故障诊断的正确率高。

Fault Diagnosis of Planetary Gear Based on CEEMDAN Sample Entropy and PNN

In order to diagnose the fault of planetary gear, the collected signals were decomposed by using complete ensemble empirical model decomposition with adaptive noise(CEEMDAN) method. The correlation coefficient of each IMF component obtained by decomposition was calculated, and the first 4 order components with greater correlation with the original signal were selected for sample entropy calculation, and the eigenvalues were obtained to form the eigenvectors. The feature vectors were put into the probabilistic neural network system for diagnosis and compared with the diagnosis results based on the local mean decomposition sample entropy feature extraction method. The results show that by using the CEEMDAN sample entropy feature extraction method, the vibration characteristics of the system can be more accurately reflected, and the fault diagnosis accuracy is higher.