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Bearing fault diagnosis based on balanced fitting transfer learning under heterologous domain samples
Published:2023-05-25 author:ZHU Xu-dong. Browse: 1258 Check PDF documents
Bearing fault diagnosis based on balanced fitting transfer learning 
under heterologous domain samples


ZHU Xu-dong

(Wuxi Institute of Art and Technology, Yixing 214200, China)


Abstract:  The number of bearing fault samples with labels is small, and there are exotic problems between the source domain data and the target domain data, which will greatly reduce the accuracy of bearing diagnosis. Therefore, the problem of bearing fault diagnosis under the condition of different source samples was studied, an iterative bearing fault diagnosis method based on improved equilibrium distribution adaptive transfer learning was proposed. Firstly, the structure of rolling bearing and the signal characteristics of faults in different parts were analyzed in detail. The working principle of transfer learning was introduced. Based on the dynamic equilibrium factor, an improved equilibrium distribution adaptation method was proposed to solve the problem of heterogeneous domain adaptation caused by the unknown difference between edge distribution and conditional distribution. Then, a pseudo-label iterative optimization method of target domain based on transfer learning and K-nearest neighbor(KNN) algorithm was proposed,and the fault labels of target domain samples were finally determined based on iterative optimization method between KNN algorithm and migration learning. Finally, the effectiveness of the diagnosis method was verified by experimental data. It was compared with other two methods to diagnose the faults of foreign samples, and the diagnostic accuracy was compared. The results show that in the bearing experiment of Case Western Reserve University(CWRU), the mean diagnostic accuracy based on transfer learning and transfer component analysis(TCA)+ KNN were respectively 93.72% and 75.52%. In the bearing experiment of Xi‘’an Jiaotong University, the diagnostic accuracy based on transfer learning and TCA+KNN was 94.80% and 70.40% respectively. The above experimental results verify the superiority of the iterative diagnosis method based on transfer learning in the fault diagnosis of samples in different source regions.

Key words:  accuracy of bearing diagnosis; heterologous domain samples; improve balanced fitting; transfer learning; Knearest neighbor (KNN)algorithm; source domain data; target domain data


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