Abstract：Aiming at the problem that the data distribution of source domain and target domain in transfer learning is very different, and it is difficult to adapt to the dynamic changes of marginal distribution and conditional distribution in traditional learning, a bearing fault diagnosis method based on deep dynamic domain adaptation is proposed. In the domain adaptation layer, a dynamic distribution adaptation method is introduced, and edge distribution alignment and conditional distribution alignment are performed by domain classifiers, and dynamic domain adaptation is performed by dynamically measuring the contribution of conditional distribution and edge distribution to the domain according to the balance factor. Through the migration diagnosis test and comparative analysis of bearing data sets of Case Western Reserve University and Jiangnan University under variable working conditions, the accuracy of cross-domain diagnosis is effectively improved, and the effectiveness and excellence of the proposed method are verified.
王军辉，雷文平，刘华杰，魏李军，韩东洋. 基于深度动态域适应的轴承故障诊断研究[J]. 振动与冲击, 2023, 42(14): 245-250.
WANG Junhui, LEI Wenping, LIU Huajie， WEI Lijun， HAN Dongyang. Bearing fault diagnosis based on deep dynamic domain adaptation. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(14): 245-250.
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