Dictionary domain adaptation transformer for cross-machine fault diagnosis of rolling bearings

Domain adaptation (DA) techniques have significantly promoted the fault diagnosis of rolling bearings by leveraging diagnostic knowledge from a labeled source domain to recognize faults in an unlabeled target domain. However, dominant DA models often suffer from inaccurate estimation of distribution discrepancies. This stems from the fact that they perform domain alignment on a batch-by-batch basis, where the distribution discrepancies are evaluated solely using mini-batch data. In this paper, a novel dictionary domain adaptation transformer (DDAT) is proposed to boost cross-machine fault diagnosis of rolling bearings. First, a feature dictionary is constructed to represent domain attributes using multi-batch data, enabling more accurate estimation of the domain gap compared to existing batch-based methods. Second, a novel dictionary adaptation framework is designed to direct the model focus on inter-domain discrepancy instead of intra-domain variations caused by random sampling in data batches. Third, a domain-shared transformer feature extractor is developed to learn domain-invariant representations by leveraging the inherent advantages of multi-head attention in capturing long-range dependencies. The proposed DDAT method conducts domain adaptation at the dictionary level, benefiting from a more accurate estimation of distribution discrepancies by leveraging the abundant and diverse data in the dictionary. Experiments confirm that the proposed DDAT method outperforms the popular deep domain adaptation models in various cross-machine diagnosis tasks of rolling bearings.