Bearing Feature Selection and Fault Diagnosis Based on Dung Beetle Optimization Algorithm and K-Nearest Neighbor Algorithm
Aiming at the problem that rolling bearings of wind turbine drive train system are easily affect-ed by redundant features in fault diagnosis,which leads to poor accuracy and efficiency of fault diagnosis.In this paper,a bearing feature selection and fault diagnosis method based on Dung Beetle Optimization(DBO)and K-Nearest Neighbors(KNN)is proposed.The method firstly extracts 20 feature data related to rolling bearing faults through time and frequency domain analysis,and then performs feature processing including normaliza-tion and feature set partitioning on the features;then seeks the optimal feature subset using the DBO algorithm with the adaptation degree of the DBO-KNN network as the target parameter;and finally verifies the optimiza-tion effect of the selected feature subset on the accuracy of the KNN classification through testing.The results of the The test results show that the two feature parameters,standard deviation and mean absolute difference,can achieve a classification accuracy of 75.10％when they are used as the input data of the KNN classifier;this method improves the fault diagnosis accuracy of the bearings while significantly reducing the number of features.
万方数据
维普
