Deep Targeted Transfer Learning Along Designable Adaptation Trajectory for Fault Diagnosis Across Different Machines

Deep transfer learning-based fault diagnosis has been developed to correct the data distribution shift, promoting a diagnosis knowledge transfer across related machines. However, there are two weaknesses: first, the assumption that all the target domain data are unlabeled is strict for robust applications of deep transfer learning to diagnosis across different machines; and second, the successes of existing methods are mostly achieved under the same conditional label distribution. For the weaknesses, this article relaxes a reasonable assumption that one-shot target domain samples called anchors are labeled, and further presents a deep targeted transfer learning (DTTL) method for tasks with different conditional label distributions. DTTL includes three parts. First, a domain-shared residual network is constructed to represent features from cross-domain data. Second, a target-domain clustering module gathers unlabeled target domain samples toward anchors. Third, a targeted adaptation module designs adaptation trajectories of target domain samples according to the associated labels of anchors and source domain data, and then corrects the joint distribution shift. The DTTL is demonstrated on transfer diagnosis tasks across different bearings. The results show that cross-domain data can be aligned by following the designable adaptation trajectories. Compared with other methods, the DTTL achieves higher diagnosis accuracy.