基于深度动态域适应的轴承故障诊断研究

Bearing fault diagnosis based on deep dynamic domain adaptation

针对迁移学习中源域,目标域数据分布差异大,传统学习中边缘分布与条件分布贡献动态变化难以适应的问题,提出了一种基于深度动态域适应的轴承故障诊断方法。在域适应层引入动态分布自适应方法,通过域分类器分别进行边缘分布对齐和条件分布对齐,在根据平衡因子动态衡量条件分布和边缘分布对域的贡献度,进行动态域适应。通过对凯斯西储大学和江南大学轴承数据集变工况下的迁移诊断试验及对比分析,有效地提高了跨域诊断的精度,验证了所提方法的有效性与卓越性。

Aiming at the problem that the data distributions in source domain and target domain in transfer learning are very different,and it is difficult to adapt to the dynamic changes of edge distribution and conditional distribution in traditional learning,a bearing fault diagnosis method based on deep dynamic domain adaptation was proposed.In the domain adaptation layer,a dynamic distribution adaptation method was introduced,and edge distribution alignment and conditional distribution alignment were performed by domain classifiers,and dynamic domain adaptation was performed by dynamically measuring the contribution of conditional distribution and edge distribution to the domain according to a balance factor.Through the migration diagnosis test and comparative analysis of the bearing data sets of Case Western Reserve University and Jiangnan University under variable working conditions,the accuracy of cross-domain diagnosis was effectively improved,and the effectiveness and excellence of the proposed method were verified.