基于GWO优化的CS-SVM轴承故障诊断

Bearing Fault Diagnosis Based on CS-SVM Model Optimized by GWO

灰狼算法(GWO)作为新型寻优算法,可用于轴承故障诊断。提出了采用GWO优化代价敏感支持向量机(CS-SVM)的诊断模型。通过经验模态分解(EMD)及主成分分析(PCA)进行特征提取并实现特征的降维,GWO优化CS-SVM参数来提升故障分类的准确率。以西储大学轴承数据为例,将比例为4∶1的训练样本和测试样本带入GWO优化的CS-SVM模型,诊断测试的准确率为96.67%,相比于传统PSO算法的准确率有所提升,收敛速度更快,表明了GWO优化的CS-SVM具有优越性。由此可以得出,GWO可用于轴承故障诊断的研究,验证了该算法模型的有效性。

Grey wolf optimizer(GWO),as a new optimization algorithm,can be used for bearing fault diagnosis.A diagnostic model was illustrated for optimize cost-sensitive support vector machine(CSSVM)by GWO.Empirical mode decomposition(EMD)and principal component analysis(PCA)were used to extract features and reduce dimensionality,GWO optimized CS-SVM parameters to improve the accuracy of fault classification.Taking bearing data of Case Western Reserve University as an example,take the training set and the test set with a ratio of 4-1 into the model.The correctness of the signal is 96.67%.Compared with the traditional PSO algorithm,the accuracy is improved and the convergence speed is faster,it shows the superiority of CS-SVM optimized by GWO.It can be concluded that the grey wolf optimizer can be used in bearing fault diagnosis research,which verifies the effectiveness of the proposed algorithm model.