Cross-domain simultaneous fault diagnosis of marine machinery with multi-source and multi-scale feature fusion

Abstract Due to the small amount and fragmented distribution of fault data, cross-domain simultaneous fault diagnosis of ship propulsion systems faces significant challenges. To address this issue, this paper proposed a novel hybrid framework, multi-source domain multi-scale joint domain adaptation multi-label classification (MMJ-DAML), for simultaneous fault diagnosis. The framework integrates multi-scale feature extraction to capture characteristics at different scales, multi-source joint domain adaptation to mitigate distribution shifts across operational conditions, and multi-label classification to model complex fault interdependencies. Experimental results on a ship degradation dataset demonstrate that MMJ-DAML achieves an average diagnostic accuracy of over 94% under diverse working conditions and domain adaptations. The study highlights the framework’s strong generalization capability in data-scarce scenarios and provides a practical solution for the simultaneous fault diagnosis of the actual ship propulsion system.