基于DIGWO-VMD-CMPE的轴承故障识别方法

Bearing fault identification based on DIGWO-VMD-CMPE

针对滚动轴承故障信号特征提取困难和识别准确率低的问题,提出了一种基于维度学习的改进灰狼优化算法(DIGWO)优化变分模态分解(VMD)和复合多尺度排列熵(CMPE)的轴承故障识别方法。首先,采用基于维度学习的狩猎(DLH)搜索策略、余弦收敛因子a和个体狼ω位置更新的方法将灰狼优化算法(GWO)改进为DIGWO,并利用DIGWO算法的自适应性优化VMD分解,得到了多个本征模态函数(IMFs);然后,利用复合多尺度排列熵计算IMFs的特征值,选取适当维数的特征,构建了故障特征向量;最后,利用DIGWO算法优化支持向量机(SVM)的惩罚系数C和径向基函数g,建立了DIGWO-SVM滚动轴承故障诊断分类器,并利用滚动轴承的振动数据验证了算法的有效性。研究结果表明:基于CMPE的DIGWO-SVM滚动轴承故障诊断方法能够有效地识别轴承的运行状况,识别准确率达到了99.42%,相较于PSO-SVM、SSA-SVM方法提高了7.75%、1.68%,证明了该方法的分类性能在滚动轴承故障诊断中更具优势。

Aiming at the difficulty of feature extraction and low recognition accuracy of rolling bearing fault signals,an improved gray wolf optimization algorithm based on dimensional learning(DIGWO)was proposed to optimize variational mode decomposition(VMD)and compound multi-scale permutation entropy(CMPE)bearing fault diagnosis method.Firstly,the grey wolf optimization algorithm(GWO)was modified into DIGWO,cosine convergence factor a and individual wolfωposition updating methods,and multiple intrinsic mode functions(IMFs)were obtained by using the adaptive optimization of VMD decomposition of DIGWO algorithm.Then,the eigenvalues of IMFs were calculated by compound multi-scale permutation entropy,and the eigenvectors of fault were constructed by selecting the features of appropriate dimension.Finally,DIGWO algorithm was used to optimize the penalty coefficient C and radial basis function g of support vector machine(SVM),and a DIGWO-SVM rolling bearing fault diagnosis classifier was established.The research results show that the CMPE-based DIGWO-SVM rolling bearing fault diagnosis method can effectively identify the running condition of bearings,and the recognition accuracy is 99.42%,which is 7.75%and 1.68%higher than PSO-SVM and SSA-SVM methods,proving that the classification performance of this method is more advantageous in the rolling bearing fault diagnosis.