Federated learning has been receiving increasing attention in the recent years, which improves model performance with data privacy among different clients. The intelligent fault diagnostic problems can be largely benefited from this emerging technology since the private data generally cannot leave local storage in the real industries. While promising federated learning performance has been achieved in the literature, most studies assume data from different clients are independent and identically distributed. In the real industrial scenarios, due to variations in machines and operating conditions, the data distributions are generally different across different clients, that significantly deteriorates the performance of federated learning. To address this issue, a federated transfer learning method is proposed in this article for machinery fault diagnostics. Under the condition that data from different clients cannot be communicated, prior distributions are proposed to indirectly bridge the domain gap. In this way, client-invariant features can be extracted for diagnostics while the data privacy is preserved. Experiments on two rotating machinery datasets are implemented for validation, and the results suggest the proposed method offers an effective and promising approach for federated transfer learning in fault diagnostic problems.

近年来，联邦学习越来越受到关注，它通过不同客户端之间的数据隐私提高了模型性能。智能故障诊断问题可以很大程度上受益于这项新兴技术，因为私有数据在现实行业中通常无法离开本地存储。虽然在文献中已经实现了有希望的联邦学习性能，但大多数研究假设来自不同客户端的数据是独立且同分布的。在实际工业场景中，由于机器和操作条件的变化，不同客户端的数据分布通常不同，这大大降低了联邦学习的性能。为了解决这个问题，本文提出了一种用于机械故障诊断的联合迁移学习方法。在来自不同客户端的数据无法通信的情况下，提出了先验分布来间接弥合域差距。通过这种方式，可以在保护数据隐私的同时提取客户端不变特征进行诊断。对两个旋转机械数据集进行了实验以进行验证，结果表明所提出的方法为故障诊断问题中的联合迁移学习提供了一种有效且有前景的方法。