Intelligent mechanical fault diagnosis techniques have been extensively developed in recent years. Owing to the advantage of data privacy protection, federated learning has recently received increasing attention; this approach can utilize monitoring data from multiple local clients to train an optimal global diagnosis model. However, low-quality data are often present for some clients, including mislabeled data and incomplete data that lack some health states. Furthermore, the data distributions are usually different across different clients owing to variations in machine operating conditions. Therefore, the performance of the federated diagnostic model may be limited by low-quality data and data distribution discrepancies. To address this issue, a federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation is proposed. First, a dynamic filter is designed by filtering out low label probabilities predicted by low-quality source domain models to construct high-confidence pseudo-labels for the target domain data. Then, the batch normalized maximum mean discrepancy (BN-MMD) distance metric is introduced into the training loss function to reduce the data distribution discrepancy between the source clients and the target client without private data leakage. While building the global model in each training round, a dynamic model aggregation algorithm is proposed to mitigate the influence of low-quality clients. This algorithm evaluates the weight of each client according to its contribution to the total consensus diagnostic knowledge and then aggregates the local models with adaptive weights. Consequently, it can overcome the drawback of the classical federated averaging (FedAvg) algorithm, where all local clients are assigned the same weight when constructing the global model. Experiments are conducted on three bearing datasets under various loads and speeds. Compared with some existing diagnostic methods, the proposed federated transfer learning method can reduce the impact of low-quality data and achieve higher diagnostic accuracy while preserving data privacy.

近年来，智能机械故障诊断技术得到了广泛的发展。由于数据隐私保护的优势，联邦学习最近受到越来越多的关注；这种方法可以利用来自多个本地客户端的监控数据来训练最佳的全局诊断模型。然而，一些客户经常会出现低质量的数据，包括错误标记的数据和缺乏某些健康状况的不完整数据。此外，由于机器运行条件的变化，不同客户端的数据分布通常不同。因此，联合诊断模型的性能可能会受到低质量数据和数据分布差异的限制。为了解决这个问题，提出了一种具有低质量知识过滤和动态模型聚合的联邦迁移学习方法。首先，通过过滤掉低质量源域模型预测的低标签概率来设计动态过滤器，为目标域数据构建高置信度伪标签。然后，将批量归一化最大平均差异（BN-MMD）距离度量引入训练损失函数，以减少源客户端和目标客户端之间的数据分布差异，而不会泄露隐私数据。在每轮训练中建立全局模型的同时，提出了一种动态模型聚合算法来减轻低质量客户端的影响。该算法根据每个客户端对总共识诊断知识的贡献来评估每个客户端的权重，然后聚合具有自适应权重的本地模型。因此，它可以克服经典联邦平均 (FedAvg) 算法的缺点，在该算法中，所有本地客户端在构建全局模型时都被分配相同的权重。在不同载荷和速度下对三个轴承数据集进行了实验。与现有的一些诊断方法相比，所提出的联邦迁移学习方法可以减少低质量数据的影响，在保护数据隐私的同时实现更高的诊断准确性。在构建全局模型时，所有本地客户端都被赋予相同的权重。在不同载荷和速度下对三个轴承数据集进行了实验。与现有的一些诊断方法相比，所提出的联邦迁移学习方法可以减少低质量数据的影响，在保护数据隐私的同时实现更高的诊断准确性。在构建全局模型时，所有本地客户端都被赋予相同的权重。在不同载荷和速度下对三个轴承数据集进行了实验。与现有的一些诊断方法相比，所提出的联邦迁移学习方法可以减少低质量数据的影响，在保护数据隐私的同时实现更高的诊断准确性。