Abstract

Recently, substantial research has explored the development of deep-learning-based methods to diagnose faults in rotating machinery. For these diagnosis methods, it is difficult to obtain high target diagnosis accuracy when the amount of labeled data obtained pertaining to the rotating machinery under study is insufficient or in cases involving a discrepancy in the distribution types found in the training and test data sets. To deal with this research need, the paper outlines a new method, a domain adaptation with semantic clustering (DASC), capable of diagnosing faults in rotating machinery. The method outlined in this research learns both domain-invariant and discriminative features. The method reduces the domain discrepancy by minimizing the domain-related loss. In addition, by defining an additional loss, which is called semantic clustering loss, and reducing it at multiple feature levels, the DASC method learns features that make samples better semantically clustered, according to their health conditions. Consequently, fault diagnosis performance for target rotating machinery can be enhanced through the use of the DASC approach. The effectiveness of the DASC approach is confirmed by examining various fault diagnosis situations with domain discrepancies across the source and target domains, using experimental data from three bearing systems. Also, various analyses are explored to better understand the advantages of the DASC method.