An increasing demand can be observed in ship-to-ship (STS) liquefied natural gas (LNG) bunkering operations, and the failures involved may lead to considerable casualties or environmental damage. For this purpose, a comprehensive methodology is proposed in this study to identify and assess these failure modes. In detail, the STS LNG bunkering process is first decomposed to develop a hierarchical structure according to systems-theoretic process analysis (STPA), the results of which serve to identify potential failure modes and their causes. Then, all the failure modes are evaluated by experts in terms of occurrence, severity, and detectability to develop a fuzzy confidential matrix, which is then transferred as an explicit confidential matrix to be weighted and normalized. Finally, the risk levels of these failure modes are analyzed by relative closeness obtained from the technique for order preference by similarity to an ideal solution (TOPSIS). This study determines nine failure modes, all of which are ranked in terms of risk level. “High pressure in vapor return line”, and “High flow rate and leakage of LNG” are determined as the top two failure modes, with risk closeness values of 0.5791 and 0.5728, respectively. “Power failure for emergency valves” is ranked as the last one, with the risk closeness value being 0.5444. Finally, suggestions are proposed according to bunkering operation guidelines to prevent or control these failure modes.

This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods.

This study presents an innovative approach for marine diesel engine fault detection, integrating unsupervised learning through Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction with time series prediction, offering significant improvements over existing methods. Unlike traditional model-based or expert-driven approaches, which struggle with complex nonlinear systems, or supervised data-driven methods limited by scarce labeled fault data, our unsupervised method establishes a normal operational baseline without requiring fault labels, enhancing applicability across diverse conditions. Leveraging UMAP’s nonlinear dimensionality reduction, the proposed method outperforms conventional linear techniques (e.g., PCA) by amplifying subtle system anomalies, enabling earlier detection of state transitions—up to two batches before deviations appear in traditional performance indicators (Ps)—thus improving fault detection sensitivity. To address nonlinear relationships in UMAP-reduced dimensions, the proposed TimeMixer-FI model enhances the TimeMixer architecture with MLP-Mixer layers. The TimeMixer-FI model demonstrates consistent improvements over the original TimeMixer across various sequence lengths, achieving an MSE reduction of 69.1% (from 0.0544 to 0.0168) and an MAE reduction of 46.3% (from 0.1023 to 0.0549) at an input sequence length of 60 time steps, thereby enhancing the reliability of the time series prediction baseline. Experimental results validate that this approach significantly enhances both the sensitivity and accuracy of early fault detection, providing a more robust and efficient solution for predictive maintenance in marine diesel engines.

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. First, the multimodal feature tensor is constructed: the fourier synchro-squeezed transform is used to convert the multisensor time-domain signals into time–frequency images, and then the tensor is reconstructed to retain the three-dimensional structural information of the sensor coupling relationship and time–frequency features. The nonlinear feature mapping strategy combined with Tucker decomposition effectively maintains the high-order correlation of the feature tensor. Second, the adaptive sample-weighting mechanism is developed: an intuitionistic fuzzy membership score assignment scheme with global–local information fusion is proposed. At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by K-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. Finally, a dual hyperplane classifier is constructed in tensor space: a structural risk regularization term is introduced to enhance the model generalization ability and a dynamic penalty factor is set to set adaptive weights for different categories. A linear equation system solving strategy is adopted: the nonparallel hyperplane optimization is converted into matrix operations to improve the computational efficiency. The extensive experimental results on the two rolling bearing datasets have verified that the proposed method outperforms existing solutions in diagnostic accuracy and stability.

To proactively identify performance anomaly evolution and potential operational risks of marine main engines and reserve a sufficient time window for maintenance intervention, this paper proposes a data-driven multi-algorithm fusion framework for performance evaluation and anomaly early warning of marine main engines. The framework first adopts a steady-state detection strategy to filter valid operating conditions and introduces the CLIQUE clustering algorithm to realize adaptive partitioning of high-dimensional operating parameters; comparative experiments with the classical K-means++ clustering algorithm demonstrate that CLIQUE achieves finer-grained operating condition classification without pre-defining the number of clusters and better adapts to the uneven distribution of actual marine engine operating conditions, which addresses the limitations of traditional single-parameter analysis and conventional dimensionality reduction methods in practical shipboard scenarios. On this basis, the Mahalanobis distance evaluation model is constructed under each partitioned operating condition, which further improves the stability and anti-interference performance of quantitative performance assessment for the main engine. Meanwhile, by integrating cumulative anomaly trend analysis and the Yamamoto mutation test, the framework accurately captures statistical mutation characteristics of the performance deviation trajectory and identifies the first mutation point as the retrospective candidate change point, forming a systematic anomaly detection mechanism. Validation using field measurement data from a 6RT-flex82T marine main engine shows that the proposed framework can comprehensively characterize the overall operating state of the main engine and capture long-term performance deviation evolution patterns. Retrospective analysis indicates that the first statistical mutation of the multivariate performance deviation precedes the significant abnormal fluctuation of a single parameter by approximately 20 days, showing the potential of providing a maintenance buffer period. The proposed method can provide technical reference and support for condition-based maintenance of marine main engines and intelligent operation and maintenance of shipboard power equipment.