Transfer learning has good ability to transfer knowledge for fault diagnosis under different working condition, while domain mismatches or domain shift can still occur during single-source domain transfer fault diagnosis. To alleviate the problem, a multi-source ensemble domain adaptation method is proposed for rotary machinery fault diagnosis. Firstly, multi-source and target domain anchor adapters are constructed based on class-central samples from multi-source domain. Secondly, multi-source ensemble domain adaptation transfer fault diagnosis model considering the mutual difference between multi-source domain is established to obtain multiple classifiers and prediction results. Then the classifiers with good performance are integrated to achieve final diagnosis model and results by ensemble of anchor adapters. Finally, the performance of the proposed method is verified by two experiments. The results show that the proposed method has ability to learn more comprehensive and general domain invariant diagnosis knowledge, significant diagnosis performance and robustness than other transfer learning methods.

迁移学习在不同的工作条件下具有良好的故障诊断知识迁移能力，而在单源域迁移故障诊断过程中仍然会出现域不匹配或域转移。为了缓解该问题，提出了一种用于旋转机械故障诊断的多源集成域自适应方法。首先，基于来自多源域的类中心样本构建多源和目标域锚适配器。其次，建立考虑多源域间相互差异的多源集成域自适应转移故障诊断模型，获得多个分类器和预测结果。然后集成性能良好的分类器，通过锚点适配器的集成来实现最终的诊断模型和结果。最后，通过两个实验验证了所提出方法的性能。结果表明，与其他迁移学习方法相比，所提出的方法能够学习更全面、更通用的领域不变诊断知识，具有显着的诊断性能和鲁棒性。