基于ELDA降维与MPA-SVM的滚动轴承故障诊断方法OA北大核心CSTPCD

In order to raise the fault diagnosis accuracy of rolling bearings,a new rolling bearing fault classification method based on Eccentric Linear Discriminant Analysis(ELDA)dimension reduction algorithm and Marine Predators Algorithm(MPA)optimized support vector machine was proposed.Firstly,the time domain and frequency domain analysis methods were used to construct the high dimension eigenmatrix of bearing signals.Then,the Adaptive Maximum Likelihood Estimation(AMLE)method was used to estimate the intrinsic dimension.The ELDA algorithm was used to extract the secondary features,so as to fully explore the sensitive features and reduce the impact of redundant features on fault diagnosis.Finally,the low-dimensional sensitive separable matrix was input into the MPA-SVM classifier to identify the fault type.Experimental analysis shows that the proposed method can effectively shorten the training time and improve the accuracy of diagnosis.