Bing Han

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.

This article addresses the safety-certified cooperative path following of networked underactuated autonomous surface vehicles (ASVs) with individual interests navigating complex marine environments. Each ASV operates under static/dynamic obstacle constraints, neighboring ASV constraints, and input constraints. A three-layer safety-certified path-following architecture is proposed based on control barrier functions and Nash equilibrium seeking. At the communication layer, a path variable update law based on distributed Nash equilibrium seeking is designed to achieve path variable cooperation. At the nominal layer, a line-of-sight-based principle is employed to design guidance laws for each ASV to achieve path following. At the optimization layer, three heading-constrained control barrier functions are developed for mapping safety constraints to constraints on yaw rates of ASVs, and a quadratic program is constructed to optimize the guidance yaw rate signal. The distinct features of the proposed method are two aspects: each ASV is allowed to consider its individual interest when performing a group task; static/dynamic obstacles and neighboring ASVs can be avoided by changing the yaw rate only. Stability analysis demonstrates that the feedback control system exhibits input-to-state stability. The efficacy of the novel method is validated through both hardware-in-the-loop simulation results and field experiment results.

With the increasing frequency of scientific expeditions to the North and South Poles and the opening of Arctic shipping routes, the number of ships navigating polar waters is gradually increasing. To avoid ship collision and ice trapped accident, the accuracy and reliability of navigation systems are crucial. However, existing navigation methods used by ships, such as inertial, astronomical, and geomagnetic, are less suitable in polar waters due to geographical and environmental constraints. The Global Navigation Satellite System (GNSS) is one of the few navigation methods available to polar vessels, but its coverage in polar waters is limited due to its orbital inclination. GNSS-Reflectometry (GNSS-R) technology utilizes GNSS satellite signals reflected by seawater and sea ice. When integrated with existing GNSS navigation technology, it can provide ships navigating polar waters with more navigation information, thereby improving accuracy and reliability. Therefore, this study proposes a ship navigation method based on GNSS-R technology to reduce the divergence of navigation results caused by the degradation of GNSS coverage in polar regions. Specifically, based on the spatial geometric relationship of GNSS-R reflection signals, a navigation model integrating GNSS reflection signals was constructed, and the existing scalar tracking loop was optimized to achieve navigation solution. The effectiveness of the proposed method was verified using navigation data from the “Tianhui” ship, part of China's 40th Antarctic scientific expedition. In waters above 75 degrees south latitude, the proposed method reduced positioning error by 16.30% compared to existing methods.

To address the issues of fault localisation and recovery in medium-voltage direct current power systems for ships, this paper proposes a hybrid reconstruction algorithm that combines the entropy weighting method with grey relational analysis, and embeds them within a framework based on chaotic particle swarm optimisation. During initialisation, the algorithm employs chaotic mapping to enhance the population diversity. The entropy weighting method is used for adaptive weighting of the objective functions, while grey relational analysis is integrated to evaluate the particle fitness and determine optimal reconstruction paths, thereby accomplishing fault diagnosis and system restoration. Simulations and case studies demonstrate that the proposed method has advantages such as rapid convergence and strong ability to escape local optima. Compared with traditional methods, it achieves a 41% increase in the reconstruction success rate and a 4.7% improvement in the system restoration capability, effectively enhancing post-fault power supply capacity and overall reliability.

In the Arctic region, the navigation and positioning accuracy of shipborne and autonomous underwater vehicle (AUV) integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) solutions is severely degraded due to poor satellite geometry, frequent ionospheric disturbances, non-Gaussian measurement noise, and strong multipath effects, as well as long-term INS-based dead-reckoning for AUVs when GNSS is unavailable underwater. In addition, the sparse ground-based augmentation infrastructure and the lack of reliable reference trajectories and dedicated test ranges in polar waters hinder the validation and performance assessment of existing marine navigation systems, further complicating the achievement of accurate and reliable navigation in this region. To improve the positioning accuracy of the GNSS/INS shipborne navigation system, this paper adopts a tightly coupled GNSS/INS navigation approach. To further enhance the accuracy and robustness of tightly coupled GNSS/INS positioning, this paper proposes an improved Adaptive Robust Extended Kalman Filter (IAREKF) algorithm to effectively suppress the effects of gross errors and non-Gaussian noise, thereby significantly enhancing the system’s robustness and positioning accuracy. First, the residuals and Mahalanobis distance are calculated using the Adaptive Robust Extended Kalman Filter (AREKF), and the chi-square test is used to assess the anomalies of the observations. Subsequently, the observation noise covariance matrix is dynamically adjusted to improve the filter’s anti-interference capability in the complex Arctic environment. However, the state estimation accuracy of AREKF is still affected by GNSS signal degradation, leading to a decrease in navigation and positioning accuracy. To further improve the robustness and positioning accuracy of the filter, this paper introduces a sliding window mechanism, which dynamically adjusts the observation noise covariance matrix using historical residual information, thereby effectively improving the system’s stability in harsh environments. Field experiments conducted on an Arctic survey vessel demonstrate that the proposed improved adaptive robust extended Kalman filter significantly enhances the robustness and accuracy of Arctic integrated navigation. In the Arctic voyages at latitudes 80.3° and 85.7°, compared to the Loosely coupled EKF, the proposed method reduced the horizontal root mean square error by 61.78% and 21.7%, respectively.

In this paper, we release a new large-scale dataset containing multiple categories of ships and floating objects at sea, which we call MASS-LSVD. It is used to train and validate target detection algorithms and future large models for ship autopiloting. The dataset was captured by a visible light camera installed aboard the world’s first intelligent research, teaching, and training ship, “Xinhongzhuan”. This MASS (maritime autonomous surface ship) was operated by Dalian Maritime University, China. We have collected more than 4000 h of video of the “Xinhongzhuan” vessel’s voyage in the Bohai Sea and other areas, which are carefully classified and filtered to cover as much as possible the various types of sample data in the marine environment, such as light intensity, weather, hull shading, data from ocean-going voyages, entering and exiting ports, etc. The dataset contains 64,263 1K-resolution images captured from video footage, covering four main ship types: Fishing Boat, Bulk Carrier, Cruise Ship, Container ship, and an ‘Other Ships’ class, for vessels that cannot be specifically classified. The dataset currently contains 64,263 pairs of 1K-resolution images covering four common ship types (fishing boat, bulk carrier, cruise ship, container, and other ship, where no specific ship type can be determined). All the images have been labeled with high-precision manual bounding boxes. In this paper, the MASS-LSVD dataset is used as the basis for training various target detection algorithms and comparing them with other datasets, which compensates for the lack of first-view images in the vessel target detection dataset, and MASS-LSVD is expected to be used to facilitate the research and application of autonomous ship navigation models in the framework of computer vision.

As a pivotal element in the maritime sector’s green transition, fuel-cell-powered ships have attracted increasing attention due to the energy efficiency and stability of their onboard powertrains. Yet, the dynamic coordination and capacity optimization of fuel cells and supercapacitors remain among the most formidable technological challenges. In this study, a hybrid marine power system pairing fuel cells with supercapacitors is devised by integrating robust control with a particle swarm optimization (PSO) algorithm. The results reveal that, under complex operating conditions, robust control effectively mitigates system uncertainties and secures reliable operation of the ship’s energy system. Optimally allocating component capacities via PSO markedly enhances the synergy between the fuel cell and the supercapacitor. Compared with conventional schemes, optimized architecture boosts energy efficiency by 12.5%, shortens response time by 8.4%, and demonstrates clear superiority in robustness and stability. This robust-control-based hybrid configuration therefore delivers outstanding performance and offers compelling guidance for the refined design of marine propulsion systems.