Most rolling element bearing (REB) fault diagnosis algorithms are evaluated on the Case Western Reserve University (CWRU) bearing dataset for its popularity and simplicity. However, the diagnosis accuracy on CWRU bearing dataset is overly saturated; it is nearly up to 100%. In this study, an input feature mappings (IFMs)-based deep residual network (ResNet) is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset. Firstly, a new data preprocessing method named as a signal-to-IFMs method is proposed to automatically extract features from raw signals without predefined parameters. Then, a deep ResNet is used as the fault classifier to learn the discriminative features from IFMs and identify the faults of REB. Finally, the proposed model is evaluated on the artificial, real, and mixed damages of the Paderborn university bearing dataset. The proposed method yields the average testing accuracies of 99.7%, 99.7%, and 99.81% in artificial, real, and mixed bearing damages, which outperforms other methods.

Accurate prediction of fuel consumption is critical for achieving efficient and low-carbon ship operations. However, the variability of the marine environment introduces significant challenges, as it leads to dynamic changes in monitoring data, complicating real-time and precise fuel consumption prediction. To address this issue, the authors proposed an incremental learning-based prediction framework to enhance adaptability to temporal dependencies in fuel consumption data. The framework dynamically adjusts a dual adaption mechanism for input features and target labels while incorporating rolling retraining to enable continuous model updates. The effectiveness of the proposed approach was validated using a real-world dataset from an LPG carrier, where it was benchmarked against conventional machine learning models, including Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Experimental results demonstrate that the proposed approach could significantly improve prediction accuracy in both offline and online scenarios. In offline mode, the proposed framework improves the R² of various machine learning models by at least 21.97%. In online mode, the proposed method increases R² by at least 17.97%. This work provides a new solution for real-time fuel consumption prediction in dynamic marine environments.

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.

Accurate fault diagnosis of marine diesel engines is essential for ensuring operational safety and optimizing maintenance strategies. This study proposes a novel fault diagnosis method based on the pre-trained large language model BART (Bidirectional and Auto-Regressive Transformers). Experimental evaluations on a publicly available fault dataset under different signal-to-noise ratio (SNR) conditions demonstrate that the BART model achieves high classification accuracy and exhibits strong robustness against noise interference. Notably, the model maintains stable performance in low-SNR environments and achieves near-perfect accuracy in high-SNR scenarios. Compared with traditional classification methods, the proposed approach not only enables accurate fault identification but also generates specific, actionable maintenance recommendations. This dual capability enhances result interpretability and provides valuable decision-support insights for predictive maintenance applications. Overall, this study underscores the potential of large language models in advancing intelligent fault diagnosis for the maritime industry.