VMD能量熵与核极限学习机在滚动轴承故障诊断中的应用

Application of VMD and hierarchical extreme learning machine in rolling bearing fault diagnosis

针对滚动轴承信号的不规则特性,致使信号故障特征难提取及难以辨识,为实现滚动轴承故障的智能诊断,提出基于VMD能量熵与核极限学习机(kernel extreme learning machine,K-ELM)的滚动轴承故障诊断方法。首先将测得振动信号进行变分模态分解(variational mode decomposition,VMD),利用能量熵进一步提取各模态特征组成高维特征向量集;然后将构建的特征向量作为K-ELM算法的输入,通过训练建立K-ELM滚动轴承故障分类模型。实验结果表明:VMD能够很好地分解轴承振动信号,且K-ELM滚动轴承故障分类模型比SVM、ELM故障分类模型具有更高的精度、更强的稳定性。

According to the irregularity characteristics of the rolling bearing signals causing the bearing condition identified difficultly, the kernel extreme learning machine fault diagnosis model is proposed. Firstly, the measured vibration signals are decomposed into variational mode decomposition, using the energy entropy to extract the features of each model to form a high dimensional feature vector set. Secondly,the combined feature vector is used as the input of the algorithm,and the fault classification model of the rolling bearing of the hierarchical limit learning machine is established. The experimental results show that the K-ELM rolling bearing fault classification model is better than ELM, and the SVM fault classification model has higher accuracy and stronger stability.