基于变分模态分解和符号熵的齿轮故障诊断方法

Gear Fault Diagnosis Method based on Variational Modal Decomposition and Symbol Entropy

为提高齿轮的故障诊断效果,提出了基于变分模态分解(Variational Modal Decomposition,VMD)和符号熵(Symbol Entropy, SE)的齿轮故障诊断方法。首先,利用VMD对齿轮故障振动信号进行分解,得到若干个本征模态分量(Intrinsic Mode Function,IMF);然后,计算IMF分量的符号熵,并将IMF符号熵组成齿轮故障特征向量;最后,将特征向量输入SVM进行故障诊断。齿轮故障诊断实测结果验证了该方法的有效性和优势。

In order to improve diagnosis accuracy of gear,a fault diagnosis method of gear based on varia.tional modal decomposition(VMD)and symbol entropy(SE)is proposed.Firstly,the gear vibration signals is decomposed into several intrinsic mode Function(IMF)which with different frequency components.Secondly,SE values of each IMF are calculated as fault feature vectors that could represent the operating conditions of gear.Finally,the fault feature is put into SVM to identify different faults.Experiment results of gear show that the proposed method has certain superiority.