Rolling bearing fault diagnosis based on dual channel CNN and SSA-SVM
TANG Bo-yu
SHAO Xing
WANG Cui-xiang
GAO Jun
Abstract：In order to effectively extract the features of rolling bearing fault signals and solve the problem that classifiers have strong dependence on the extracted features,this paper proposes a rolling bearing fault diagnosis method combing dual-channel feature fusion convolutional neural network(CNN)and support vector machine optimized by sparrow search algorithm(SSA-SVM).The features of bearing data are extracted by establishing a parallel dual channel structure of one-dimensional CNN and two-dimensional CNN.Then the fault features extracted by dual channel structure are fused in the fusion layer,and the results of full connection layer are used as the input of SSA-SVM.In addition,SSA is exploited to optimize the parameters of SVM to improve the classification accuracy.Finally,the proposed method is compared with the traditional one-dimensional CNN and two-dimensional CNN using Case Western Reserve University bearing data set to ver-ify its effectiveness.Experimental results demonstrate that the proposed method has higher fault identification accuracy.All code and experimental results of the method have been open source and available at
Keywords：fault diagnosisrolling bearingconvolutional neural networksupport vector machine
Publication Date：2024-09-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 1626-1635 )