ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION k-SUB CONVEX HULL
HU AiRu
WU ZhanTao
YANG Yu
CHENG JunSheng
Abstract：Feature selection and classifier design are often studied separately in rolling bearing fault diagnosis,so it is difficult to obtain satisfactory classification accuracy.An adaptive feature selection k-sub convex hull(AFSKCH)classification model was proposed by combining feature selection and classifier optimization,which realized the integration of adaptive feature selection and classification.Firstly,the convex hull distance function was used to maintain the local neighborhood structure on the data manifold,and the feature weight matrix was obtained by alternately constructing k-sub convex hulls.Secondly,the distance was solved by the method of linear programming proximity,and the adaptive feature space was obtained by using the multiplier alternating direction method.Finally,the classification was carried out according to the minimum reconstruction distance from the test point to the k-sub convex hull.The analysis results of rolling bearing fault vibration signals show that the feature selection performance of this method is better than other feature selection methods,and the classification accuracy is higher.
Keywords：Adaptive feature selectionNeighborhood embeddingk-sub convex hullRolling bearingFault diagnosis
Publication Date：2024-04-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 255-263 )