Abstract: The fault data of bearings exhibited significant distribution discrepancies under varying operating conditions， relatively low diagnostic accuracy was resulted in practical fault detection models. Additionally， most existing research on cross-domain bearing fault diagnosis primarily emphasized inter-domain alignment and intra-class comparison， while neglecting the influences of interactions between subdomains. Therefore， a cross-domain fault diagnosis method of bearings was proposed based on joint subdomain contrast alignment. In order to highlight the fault features， the bearing vibration signals were transformed into time-frequency graph by short-time Fourier transform， and the fault features were obtained by inputting them into the feature extraction module. Domain adaptation methods achieved cross-domain recognition by transferring knowledge learned from the source domain to the target domain. During the domain adaptation processes， a joint subdomain contrast alignment strategy was used to bring samples from the same subdomain closer together while separating samples from different subdomains， which aligned the subdomain distributions of the same class samples among the source and target domains， thereby enhancing the models generalization ability in the target domain. Resnet34 was used as the feature extraction network on the model architecture， and the maximum mean difference was used at the output of the network to align the global distribution of the source domain and the target domain. Compared with the classical domain adaptation methods， the experimental results on the bearing fault data set of Case Western Reserve University shows that the cross-domain fault diagnosis method of bearings based on joint subdomain contrast alignment has better feature transfer ability. 

Key words:  , fault diagnosis, rolling bearing, transfer learning, contrast alignment, subdomain adaptation

摘要： 变工况下轴承故障数据分布差异大，故障诊断模型实际识别精度较低，且目前大多数轴承跨域故障诊断研究侧重于域间对齐和类内对比而忽略了子域间的影响，为此，提出了一种基于联合子域对比对齐的轴承跨域故障诊断方法。为突出故障特征，对轴承振动信号进行短时傅里叶变换将其转化为时频图，输入特征提取模块，得到故障特征，并通过领域自适应方法将从源域学到的知识迁移到目标域，实现跨域识别。在域适应过程中采用联合子域对比对齐策略，将来自相同子域样本拉近的同时分开不同子域样本，对齐源域与目标域中同类别样本所属子域分布，从而提高模型在目标域上的泛化能力。在模型架构上使用Resnet34作为特征提取网络，在网络输出端使用最大均值差异，对齐源域与目标域的全局分布。基于凯斯西储大学轴承故障数据集进行实验验证，与经典域适应方法进行对比，实验结果表明基于联合子域对比对齐的轴承跨域故障诊断方法具有更好的特征迁移能力。

关键词: 故障诊断, 滚动轴承, 迁移学习, 对比对齐, 子域自适应