An Uncertainty-Aware Cross-Modal Fusion Network for Domain Generalization in Mechanical Fault Diagnosis

Condition monitoring of modern industrial machinery increasingly relies on data-driven deep learning methods. However, domain shifts caused by dynamic working conditions often lead to substantial accuracy degradation in unseen target domains. Single-modality sensing also suffers from inherent perception limitations under heavy interference. To address these issues, this paper proposes a multi-modal domain generalization framework named the uncertainty-aware cross-modal fusion network (UCFNet). The framework incorporates three key modules. Specifically, a parallel mixed attention strategy captures fine-grained cross-modal interactions and intra-modal context, thereby extracting domain-invariant representations. In addition, a dynamic collaborative gating mechanism filters out feature redundancy and suppresses interference caused by working condition shifts. Furthermore, an uncertainty-aware feature fusion module estimates the prediction confidence of each modality and uses it to guide decision fusion in unseen domains. Comprehensive experiments are conducted on reciprocating compressor and wheelset bearing datasets. Experimental tasks evaluate both multi-source domain generalization and single-source domain generalization. The results demonstrate that UCFNet achieves superior performance compared with state-of-the-art methods.