【摘要】 针对不同工况下滚动轴承振动数据分布差异大、部分工况下的带标签数据难以获取、不同用户间数据不共享、单一用户数据量少，导致建立诊断模型准确率不高的问题，提出一种联邦特征迁移学习框架以及基于联邦多表示域适应的不同工况下滚动轴承故障诊断方法。该方法对滚动轴承时域振动数据做小波变换得到时频谱图，将先验的有标签公共数据作为源域，多用户无标签孤岛隐私数据作为目标域；引入多表示特征提取结构对原始残差网络进行改进，提取源域和目标域的多表示特征，分别构建多用户本地模型；使用深度神经网络的模型压缩思想改进联邦迁移学习框架中的参数传递策略，增强联邦框架的安全性并降低通信开销；在服务器端构建可用于不同工况下滚动轴承故障诊断的联邦全局模型。经两种轴承数据集的实验验证，所提方法无需多用户共享数据即可整合孤岛数据知识，建立有效的不同工况下滚动轴承故障诊断模型，平均故障诊断准确率可达97.6%,相比单一用户建模提升至少3.2%。更多还原

【Abstract】 To address problems of large distribution difference in rolling bearing vibration data under different working conditions, difficulty in obtaining labeled vibration data under certain working conditions, the non-sharing of data among different users and the small amount of single user data, which lead to the low accuracy of the established diagnosis model, a federated feature transfer learning framework and a fault diagnosis method of rolling bearing under different working conditions based on the federated multi-representation adaptation are proposed. The time domain vibration data of rolling bearings are transformed by wavelet transform and the time-frequency spectrum can be obtained. The priori labeled public data are used as the source domain and the multi-user unlabeled privacy silos data are used as the target domain. A multi-representation feature extraction architecture is introduced to improve the original residual network, multi-representation features of source domain and target domain are extracted, and multi-user local models are constructed respectively. To enhance the security of the federated framework and reduce the communication overhead, the deep neural network model compression idea is used to improve the parameter transfer strategy in the federated transfer learning framework. A federated global model for rolling bearing fault diagnosis under different working conditions is formulated on the server side. On two bearing datasets, experimental results show that the proposed method can integrate soils data knowledge without multi-user sharing data, and establish an effective fault diagnosis model of rolling bearings under different working conditions, the average fault diagnosis accuracy canreach 97.6%, which is at least 3.2% higher than the single user modeling.which has high accuracy and strong generalization.更多还原