针对基于域间分布差异度量的域适应方法忽视两个域中类与类之间条件分布差异对域间分布差异度量的影响，而导致知识迁移精度低的问题。以跨旋转机械的滚动轴承为研究对象，提出一种基于一维卷积自编码器的条件分布域适应的故障诊断方法。该方法通过构建一种基于阈值局部子域相关性对齐的域间差异度量准则（T-LCORAL），并将该准则嵌入到自编码器的输出层，以有效引导模型进行特征提取。然后，利用伪标签将源域和目标域划分成多个子域进行域内细粒度对齐，并通过可靠性阈值对伪标签进行筛选，以提高域中类与类之间边界分布的可判别性。最后，采用三个轴承数据集进行跨机械的轴承故障迁移诊断实验，结果表明所提方法是具有良好的性能。

Most of existing domain adaptation-based scenarios used to estimate inter-domain distribution discrepancy can ignore the impact aroused by conditional distribution discrepancy between classes in two domains on the measurement of inter-domain distribution discrepancy, which can make the accuracy of knowledge transfer drop dramatically. A fault diagnosis method based on one-dimensional convolutional auto-encoder and conditional distribution domain adaptation is proposed for rolling bearings across rotating machinery. An inter-domain dissimilarity criterion (T-LCORAL) based on threshold local subdomain correlation alignment is constructed, which is embedded into the output layer of the autoencoder to guide the model for feature extraction effectively. Then, pseudo labels are employed to divide the source and target domains into multiple subdomains for fine-grained alignment within the domain, Moreover, the pseudo labels are screened by reliability thresholds to improve the discriminability of the boundary distribution between classes in the domain. Finally, three bearing datasets were used for cross mechanical bearing fault transfer diagnosis experiments, and the results showed that the proposed method can achieved better performance.