基于参数优化VMD和样本熵的滚动轴承故障诊断EI北大核心CSCD

针对滚动轴承故障特征提取不丰富而导致的诊断识别率低的情况,提出了基于参数优化变分模态分解(Variational mode decomposition,VMD)和样本熵的特征提取方法,采用支持向量机(Support vector machine,SVM)进行故障识别.VMD方法的分解效果受限于分解个数和惩罚因子的选取,本文分析了这两个影响参数选取的不规律性,采用遗传变异粒子群算法进行参数优化,利用参数优化的VMD方法处理故障信号.样本熵在衡量滚动轴承振动信号的复杂度时,得到的熵值并不总是和信号的复杂度相关,故结合滚动轴承的故障机理,提出基于滚动轴承故障机理的样本熵,此样本熵衡量振动信号的复杂度与机理分析的结果一致.仿真实验表明,利用本文提出的特征提取方法,滚动轴承的故障诊断准确率有明显的提高.

Rolling Bearing Fault Diagnosis Based on Parameter Optimization VMD and Sample Entropy

In this paper, aiming at the low diagnostic recognition rate caused by insufficient fault feature extraction of rolling bearings, a feature extraction method based on parameter optimization variational mode decomposition (VMD) and sample entropy is proposed. Support vector machine (SVM) is used for fault identification. The decomposition effect of VMD method is limited by the number of decomposition and the selection of penalty factor. This paper analyses the irregularity of the two influencing parameters, uses genetic mutation particle swarm optimization to optimize the parameters, and uses parameter optimization VMD method to process fault signals. Sample entropy is not always related to the complexity of the signal when it is used to measure the complexity of the vibration signal of rolling bearings. Therefore, according to the fault mechanism of rolling bearings, a sample entropy based on the fault mechanism of rolling bearings is proposed, which is consistent with the result of mechanism analysis. The simulation results show that the fault diagnosis accuracy of rolling bearings is obviously improved by using the feature extraction method proposed in this paper.