In recent years, although traditional intelligent fault diagnosis methods have achieved satisfactory development in transfer learning tasks, the sample information that the single client can generally provide is extremely limited in real industrial scenarios. And the private data needs to be guaranteed not to leave the local storage during the application process, which leads to obstacles for fault diagnosis methods to solve cross-device and cross-scenario client transfer tasks. Therefore, a novel federated transfer learning strategy based on dual-correction training (FTSDC) is proposed, which enables fault diagnosis for multi-device tasks without target domain samples to participate in model training. The dual correction training of source client is proposed to enhance the local network’s generality, which involves two links: The multiple-functions correction model hyperparameters link and the transition training correction network attention area link. Multiple sets of optimization functions are introduced into local network training to reduce the covariate drift phenomenon caused by domain discrepancies. And the local model fine-tunes the parameters of the trained network through the transition dataset to correct attention areas. Furthermore, The central server evaluates each source client model according to the contribution of the local model to the transfer strategy, and the local model parameters are cumulatively optimized by referring to the diagnosic experience of the global model. It is worth noting that the trained local model can still demonstrate commendable performance even when faced with more complex scenarios. Finally, the proposed scheme was rigorously assessed against diverse federated learning methods across various task settings during the experimental session. The results confirmed the effectiveness of the proposed scheme in safeguarding the data privacy of each client while concurrently enhancing the diagnostic accuracy of the target task.

中文翻译：

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FTSDC：一种基于双校正训练的轴承跨机故障诊断联合迁移学习策略

近年来，虽然传统的智能故障诊断方法在迁移学习任务中取得了令人满意的发展，但在实际的工业场景中，单个客户端一般可以提供的样本信息极其有限。并且在应用过程中需要保证私有数据不离开本地存储，这给故障诊断方法解决跨设备、跨场景的客户端传输任务带来了障碍。因此，提出了一种基于双校正训练（FTSDC）的新型联邦迁移学习策略，能够在没有目标域样本参与模型训练的情况下实现多设备任务的故障诊断。为了增强本地网络的通用性，提出了源客户端的双校正训练，其中涉及两个环节：多功能校正模型超参数环节和过渡训练校正网络注意区域环节。在局部网络训练中引入多组优化函数，以减少域差异引起的协变量漂移现象。并且局部模型通过转换数据集微调训练网络的参数以纠正注意力区域。此外，中央服务器根据本地模型对传输策略的贡献来评估每个源客户端模型，并参考全局模型的诊断经验来累积优化本地模型参数。值得注意的是，即使面对更复杂的场景，经过训练的本地模型仍然可以表现出值得称赞的性能。最后，在实验过程中，针对不同任务设置中的多种联邦学习方法对所提出的方案进行了严格评估。结果证实了所提出的方案在保护每个客户端的数据隐私的同时提高了目标任务的诊断准确性的有效性。