基于最小熵解卷积-变分模态分解和优化支持向量机的滚动轴承故障诊断方法

提出了一种基于最小熵解卷积和变分模态分解以及模糊近似熵的故障特征提取方法，并采用优化支持向量机对故障进行识别分类。首先利用最小熵解卷积方法降低噪声干扰并增强故障信号中故障特征信息，进而对降噪后的信号进行变分模态分解，并利用模糊近似熵量化变分模态分解后包含故障特征信息的模态分量以构建特征向量，之后通过采用扩展粒子群算法优化惩罚因子和核函数参数的支持向量机，对故障样本训练并完成故障识别分类。将所提方法应用于滚动轴承不同损伤程度、不同故障部位的实验数据，验证了该方法的有效性。与基于局部均值分解的特征提取方法相对比，结果表明所提方法可以更精确地提取出滚动轴承故障特征，并能够更准确地完成不同故障的识别；通过与基于网格寻优算法优化的支持向量机方法和基于扩展粒子群优化的最小二乘支持向量机方法相对比，结果表明所提方法具有更好的分类性能，能达到更好的诊断效果。

Fault Diagnosis Method Based on MED-VMD and Optimized SVM for Rolling Bearings

A method of fault feature extraction was proposed based on MED, VMD and fuzzy approximate entropy, and the optimized SVM was used to identify faults. The MED method was used to reduce the noise interferences and to enhance the fault feature informations in the fault signals, and the signals after noise reduction by VMD were decomposed, then, the fuzzy approximation entropy was used to quantify the modal components of fault feature informations after VMD, and the feature vectors were constructed, Finally, the extended particle swarm optimization(EPSO) algorithm was used to optimize the penalty factors and the kernel function parameters of SVM to complete the fault recognition classification. The proposed method was applied to the experimental data of rolling bearings, and the effectiveness of the method was verified. Compared with the feature extraction method based on local mean decomposition(LMD), it is shown that the proposed method may extract the features of rolling bearing faults more accurately and may identify different faults more accurately. Compared with SVM based on grid search algorithm and the least square support vector machines(LSSVM) based on EPSO algorithm, the proposed method has better classification performance and better diagnosis performance.