仿真数据驱动的改进无监督域适应轴承故障诊断

现有无监督的轴承跨域故障诊断研究往往采用充足的试验台数据构建源域，且难以兼顾领域间的边缘分布和条件分布对齐，此外在域适配过程中全体源域样本被赋予相同的重要性。针对以上挑战，提出了一种仿真数据驱动的改进无监督域适应轴承故障诊断新方法。采用仿真所得的故障信息丰富，标签数据充足的轴承故障数据构建源域，降低对试验台资源的依赖。设计了一种嵌入联合最大均值差异的改进损失函数，在无监督场景下实现了不同域间边缘分布和条件分布的同时对齐。开发了一种源域样本权值分配机制，通过领域预测误差衡量源域样本与目标域样本的相似性从而自适应地分配其权值以抑制负迁移。使用两组试验台数据作为目标域对所提方法进行验证，结果表明：所提方法能够充分适配仿真域和实验域的深层特征分布，提高无监督跨域场景下的故障诊断精度。

Simulation Data-driven Enhanced Unsupervised Domain Adaptation for Bearing Fault Diagnosis

The existing unsupervised cross-domain fault diagnosis studies of bearing usually utilize sufficient experimental data collected from test rigs as the source domains, the marginal distribution and conditional distribution alignments between domains are difficult to be considered simultaneously, and all source-domain samples are endowed with the same importance in the process of domain adaptation. Aiming at the above challenges, a new method of simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis is proposed. The bearing fault data with rich fault information and sufficient label data obtained by numerical simulation is used to construct the source domain, thus reducing the dependence on the resources of test rigs. An enhanced loss function embedded with the joint max mean discrepancy is designed to achieve simultaneous alignments of marginal and conditional distributions between different domains in unsupervised scenarios. A weight allocation mechanism for source domain samples is developed to measure the similarity between each individual source domain sample and target domain samples through domain prediction error and to adaptively allocate their weights to suppress negative transfer. Two sets of experimental data collected from test rigs are used as the target domains to validate the effectiveness of the proposed method. The results show that the proposed method can fully adapt the deep feature distributions of simulation domain and experimental domain to improve cross-domain fault diagnosis accuracy in unsupervised scenarios.