Fault diagnosis of rolling bearings based on full vector CEEMDAN energy moment and AMHSSA-SVM
In order to make full use of the fault characteristic information of rolling bearings and improve the accuracy and re-liability of fault diagnosis,A Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise,complementary en-semble empirical mode decomposition with adaptive noise,CEEMDAN Energy Moment and Adaptive Multi-population Hybrid Sparrow Search Algorithm,AMHSSA optimizes fault diagnosis methods of Support Vector Machine(SVM).First,homologous two-channel signals are fused by full vector spectrum technique.Secondly,the CEEMDAN algorithm is used to process the fusion signal,and the first 5 IMF components with large correlation coefficients are selected,and their energy moments are calculated as the feature inputs of the SVM model.Finally,AMHSSA algorithm is proposed and parameters of support vector machine model are optimized,and AMHSSA-SVM fault diagnosis model is established.The test results show that this model can effectively im-prove the recognition accuracy.Compared with similar models,this model further proves its superiority in classification accuracy and optimization time.
rolling bearingfault diagnosisfull vector spectrumCEEMDANAMHSSASVM
万方数据
维普
