一种参数优化VMD多尺度熵的轴承故障诊断新方法

A new fault diagnosis approach for bearing based on multi-scale entropy of the optimized VMD

现有基于变分模态分解算法(VMD)的轴承故障诊断方法,由于其参数K需要依据先验知识预先设定,缺乏对K值最优设定的理论支撑,难以保证故障特征提取及故障诊断的精确性.针对上述问题,提出一种基于参数估计优化的VMD与多尺度熵(MSE)的石化装备轴承特征提取及诊断新方法.首先,针对VMD分解参数K的难以实现最优设定问题,利用局部均值分解(LMD)自适应分解分量的频率分布特征,构建一种实现K值有效估计的方法;其次,在VMD分解的基础上,提出一种MSE和线性判别分析(LDA)协同特征提取方法,完成特征模型构建;然后,针对轴承故障特征样本过少,利用支持向量机(SVM)对提取故障特征进行识别;最后,利用石化装备实验室仿真平台的轴承故障数据进行实验,验证算法的有效性和工程实用性.对比分析表明,所提出的算法可以很好地提取故障特征且故障识别精度较高,具有较好工程操作性和扩展性.

It is well known that the parameter K of variational mode decomposition(VMD)needs to be preset according to prior knowledge without theoretical support for optimal setting.Thus,for the existing fault diagnosis methods for bearing based on VMD,the correctness of characteristic extraction and accuracy of fault diagnosis are extremely difficult to be guaranteed.To solve this problem,a novel collaborative diagnosis approach of petrochemical equipment for bearing based on optimal VMD and multiscale entropy(MSE)is proposed.Firstly,because optimizing the decomposition parameter K for VMD is difficult,an effective estimation model of K is constructed according to the frequency distribution characteristics of the decomposition components of local mean decomposition(LMD).Then,a novel characteristic extraction technique collaborating MSE and linear discriminant analysis(LDA)is proposed to establish characteristic samples.Furthermore,aiming at the fault characteristic of small samples for bearing,support vector machine(SVM)is introduced to identify the fault characteristics.Finally,the bearing fault data collected from the simulation platform of the petrochemical equipment laboratory is used to verify the effectiveness and engineering practicability of the proposed approach.The comparative analysises show that the proposed algorithm can effectively diagnose faults of the bearing with good engineering operability and scalability.