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 https://github.com/suisuisuiaa/tbysuisui.
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