基于VMD与分层极限学习机的滚动轴承故障诊断方法研究

摘要

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的问题,为实现滚动轴承故障的智能诊断,提出基于VMD排列熵与分层极限学习机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),利用排列熵进一步提取各模态特征组成高维特征向量集;其次利用自动编码器(Automatic Encoder,AE)对极限学习机的隐含层进行分层,且使隐含层节点的输入权值和阈值满足正交条件;最后将构建的特征向量作为H-ELM算法的输入,通过训练建立H-ELM滚动轴承故障分类模型.实验结果表明:H-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,cause the bearing condition is difficult to identify,and the hierarchical extreme learning machine fault diagnosis model is proposed.Firstly,the measured vibration signals are decomposed into variational mode decomposition,using the permutation entropy to extract the features of each model to form a high dimensional feature vector set;Secondly,the hidden layer of the extreme learning machine is layered by using automatic encoder,and the input value and threshold value of the hidden layer nodes are satisfied;Finally,the combined feature vector is used as the input of the algorithm,and the fault classification model of the rolling bearing of the hierarchical extreme learning machine is established.The experimental results show that the H-ELM rolling bearing fault classification model is better than ELM,and the SVM fault classification model has higher accuracy and stronger stability.