滚动轴承的VMD-LSSVM故障识别方法

VMD-LSSVM Fault Identification Method for Rolling Bearings

滚动轴承是工程设备中的关键部件,对滚动轴承进行故障识别方法研究有重要的意义。为了解决滚动轴承振动信号分析能力薄弱的问题,提出了一种基于变分模态分解(Variational mode decomposition,VMD)与最小二乘支持向量机(Least square support vector machine,LSSVM)的滚动轴承故障识别方法。以凯斯西储大学滚动轴承实验数据为研究对象,获取4类故障7种滚动轴承状态实验振动数据。进行VMD分解,得出最佳分解本征模态函数(Intrinsic mode function,IMF)个数4,然后计算4个IMF样本熵(Sample entropy,SE)得到相应特征量,输入LSSVM模型进行状态识别。实验表明,基于VMD-LSSVM的方法比EMD(Empirical mode decomposition)-HMM(Hidden Markov model)和EMD-LSSVM方法有更高的识别率。

Rolling bearing is a key component in engineering equipment,the research on fault identification method of rolling bearings is of great significance.In order to solve the problem of weak ability of rolling bearing vibration signal analysis,a rolling bearing fault identification method based on variational mode decomposition(VMD)and least square support vector machine(LSSVM)is proposed.Taking the rolling bearing experimental data of Case Western Reserve University as the research object,the experimental vibration data of four types of faults and seven rolling bearing states are obtained.VMD decomposition is carried out to obtain the number 4 of the best decomposed intrinsic mode function(IMF),and then the four IMF sample entropy(SE)is calculated to obtain the corresponding feature quantity,which is input into the LSSVM model for state recognition.Experiments show that the proposed method based on VMD-LSSVM has higher recognition rate than EMD(empirical mode decomposition)-HMM(Hidden Markov model)and EMD-LSSVM.