【摘要】 滚动轴承早期故障信号易受噪声干扰，故障冲击成分难以提取，故障识别困难。为从多角度提取故障轴承振动信号特征参数，利用变分模态分解(VMD)将振动信号分解为若干本征模态分量(IMFs),基于包络熵、相关系数、峭度筛选IMF分量。提取所选IMF的时域和频域特征、信号VMD能量熵及各IMF能量比组成特征向量，从时域、频域和能量角度反映故障信息。使用麻雀搜索算法(SSA)优化SVM参数，确定最优参数，克服参数选择难题。将样本特征向量输入SSA-SVM中进行故障分类，轴承故障实验数据表明：该方法故障识别平均准确率在98.71%以上；与单一域特征相比，该方法对故障类型和损伤程度识别效果更佳。更多还原

【Abstract】 Early fault signals of rolling bearings are easily interfered by noise, and fault impact components are difficult to be extracted and fault identification is difficult.In order to extract characteristic parameters from multi-angle fault bearing vibration signals, the vibration signals were decomposed into several intrinsic mode functions(IMFs) using variational mode decomposition(VMD),and IMF components were screened based on envelope entropy, correlation coefficient and kurtosis.The time domain and frequency domain features of selected IMF,VMD energy entropy of signal and energy ratio of each IMF were extracted to form feature vectors, and the fault information was reflected from time domain, frequency domain and energy perspective.The sparrow search algorithm(SSA) was used to optimize SVM parameters and determine the optimal parameters to overcome the problem of parameter selection.The sample feature vectors were input into SSA-SVM for fault classification.The experimental data of bearing faults show that the average accuracy rate of fault identification by this method is above 98.71%.Compared with single domain feature, this method has better recognition effect on fault type and damage degree.更多还原