Search Results (33)

The backbone of maritime transportation has always been the successful execution of ship operations. However, the human factor has proven to be a weak point in the system. To reduce and mitigate it, a regulatory framework and consequently a safety system for ship barriers were created and implemented with this goal in mind. The expected result of these measures was the creation of a resilient maritime transport system. Nevertheless, the available statistics show that most of the reported accidents and incidents occurred during ship operation, with the human factor as the main cause. Therefore, it is useful to investigate whether the regulatory framework can influence the safety system of ship barriers. Therefore, the objectives of the study are as follows: (a) to investigate and determine the regulatory safety requirements and the elements related to the ship barrier system, and (b) to investigate the influence of the regulatory safety requirements on the elements related to the ship barrier system. From the data obtained and the analyses performed, seven factors emerged. Four of them were related to the regulatory requirements and three to the shipboard barrier system, a basis for the presented models. Several important findings were obtained that have theoretical and practical implications and further highlight the importance and potential undesirable side effects of the provisions of the current regulatory framework. Full article

The winch traction system for shipboard aircraft, when operating in a marine environment, is subjected to additional forces and moments due to the complex motion of the hull. These loads pose significant threats to the safety of the aircraft during the traction process. To address the safety issues under complex sea conditions, this paper adopts harmonic functions to describe the rolling, pitching, and heaving motions of the hull. A theoretical analytical model of the three-winch traction system, considering the intricate coupling motions of the ship, is established. Unlike previous studies that often simplify ship motion or focus on single-component modeling, this work develops a complete, whole-system dynamic model integrating the winch system, rope, aircraft structure, and ship interaction. The dynamic characteristics of the small-deck winch traction system are investigated, with particular focus on the influence of the rear winch position, driving trajectory, and ship motion on the system’s dynamics and safety. This research is innovative in systematically exploring the dynamic safety behavior of a three-winch traction system operating under small-deck conditions and complex sea states. The results show that as the distance between the two rear winches increases, the lateral force on the tire decreases. Additionally, as the aircraft’s turning angle increases, the front winch rope force also increases. Moreover, with higher sea condition levels and wind scales, the maximum lateral force on the tires increases, leading to a significant reduction in the stability and safety of the winch traction system. This is particularly critical when the sea condition level exceeds 3 and the wind scale exceeds 6, as it increases the risk of tire sideslip or off-ground events. This research has substantial value for enhancing the safety and stability of winch traction systems on small decks, and also provides a theoretical basis for traction path design, winch position optimization, and the extension of the service life of key system components, demonstrating strong engineering applicability. Full article

This study aims to evaluate the techno-economic feasibility of implementing the IEC 62034:2012 standard, which governs automatic test systems for battery-powered emergency escape lighting, on a 52.8-m multipurpose vessel. The work is based on a detailed case study of the vessel’s lighting systems, incorporating lighting simulations, system modifications using DALI-compatible components, and an economic analysis based on net present value, internal rate of return, and discounted payback period. The results demonstrate that the implementation reduces preventive maintenance costs significantly—from 24,750 EUR to 2250 EUR over ten years—while achieving a positive net present value of 5317 EUR, an internal rate of return of 27.81%, and a discounted payback period of under five years. The findings contribute to maritime safety literature by extending the application of IEC 62034:2012 to shipboard environments, where it is not yet standard practice. Practically, it provides a cost-effective and safety-enhancing solution for ship operators, suggesting that automated testing systems can replace outdated manual maintenance procedures and improve compliance with safety regulations. Full article

When ships conduct offshore operations in the ocean, they are subject to disturbances from natural factors such as sea breezes and waves. These disturbances lead to movements detrimental to the ship’s stability, especially heave movement in the vertical direction, which profoundly impacts the safety of shipboard facilities and staff. To counter this, the active wave compensation device is widely used on ships to maintain the stability of the working environment. However, the system’s efficiency and accuracy are compromised by the significant delay incurred while obtaining real-time motion signals and driving the actuator for motion compensation. To solve the time delay problem of shipborne wave compensation equipment in motion compensation under complex sea conditions, it is necessary to improve the ship heave motion prediction accuracy in an active wave compensation system. This paper presents a prediction method of ship heave motion based on the particle swarm optimization (PSO) and convolutional neural network–long short-term memory (CNN-LSTM) hybrid prediction model. The paper begins by establishing the ship heave motion model based on the P–M spectrum and slice theory, simulating the ship heave motion curve under different sea conditions on MATLAB. This simulation provides crucial data for the subsequent prediction model. The paper then delves into the realization method of ship heave motion based on PSO-CNN-LSTM, where the convolutional neural network (CNN) is used to extract the features of the input signal, thereby enhancing the multi-source feature fusion ability of the LSTM neural network model. The PSO algorithm is then employed to optimize the network structure and hyperparameters of the convolutional neural network. The experiments demonstrate that the proposed PSO-CNN-LSTM hybrid model effectively addresses the problem of predicting drift and boasts significantly higher prediction accuracy, making it suitable for predicting the short-term heave motion of ships. The data show that the optimized root mean square error (RMSE) value under level 5 sea conditions is 0.01265 compared to 0.01673 before optimization, and the optimized RMSE value under level 6 sea conditions is 0.01140 compared to 0.01479 before optimization, which demonstrates that the error between the predicted value and the actual value of the model decreases. This improved accuracy provides reassurance in the model’s predictive capabilities and lays the foundation for improving the accuracy of the motion compensation system in the future. Full article

Driven by increasingly stringent carbon emission regulations from the International Maritime Organization (IMO), the maritime industry increasingly requires eco-friendly power systems and enhanced energy efficiency. Lithium-ion batteries, a core component of these systems, necessitate precise temperature management to ensure safety, performance, and longevity, especially under high-temperature conditions owing to the inherent risk of thermal runaway. This study proposes a sensorless temperature estimation method using a long short-term memory network. Using key parameters, including state of charge, voltage, current, C-rate, and depth of discharge, a MATLAB-based analysis program was developed to model battery dynamics. The proposed method enables real-time internal temperature estimation without physical sensors, demonstrating improved accuracy via data-driven learning. Operational data from the training vessel Hannara were used to develop an integrated organic Rankine cycle–energy storage system model, analyze factors influencing battery temperature, and inform optimized battery operation strategies. The results highlight the potential of the proposed method to enhance the safety and efficiency of shipboard battery systems, thereby contributing to the achievement of the IMO’s carbon reduction goals. Full article

Sea ice and the resulting loads are major safety concerns for vessels operating in ice-covered regions. This study presents a tailored sea ice and local ice load monitoring system specifically designed for polar cargo vessels. The system employs shipboard cameras coupled with a DeepLab v3+-based algorithm to achieve real-time ice concentration identification, demonstrating 90.68% accuracy when validated against historical Arctic Sea ice imagery. For structural load monitoring, we developed a hybrid methodology integrating numerical simulations, full-scale strain measurements, and classification society standards, enabling the precise evaluation of ice-induced structural responses. The system’s operational process is demonstrated through comprehensive case studies of characteristic ice collision scenarios. Furthermore, this system serves as an exemplary implementation of a navigation assistance framework for polar cargo vessels, offering both real-time operational guidance and long-term reference data for enhancing ice navigation safety. Full article

In this study, we propose an advanced object detection model for fire and smoke detection in maritime environments, leveraging the DETR (Detection with Transformers) framework. To address the specific challenges of shipboard fire and smoke detection, such as varying lighting conditions, occlusions, and the complex structure of ships, we enhance the baseline DETR model by integrating EfficientNet-B0 as the backbone. This modification aims to improve detection accuracy while maintaining computational efficiency. We utilize a custom dataset of fire and smoke images captured from diverse shipboard environments, incorporating a range of data augmentation techniques to increase model robustness. The proposed model is evaluated against the baseline DETR and YOLOv5 variants, showing significant improvements in Average Precision (AP), especially in detecting small and medium-sized objects. Our model achieves a superior AP score of 38.7 and outperforms alternative models across multiple IoU thresholds (AP50, AP75), particularly in scenarios requiring high precision for small and occluded objects. The experimental results highlight the model’s efficacy in early fire and smoke detection, demonstrating its potential for deployment in real-time maritime safety monitoring systems. These findings provide a foundation for future research aimed at enhancing object detection in challenging maritime environments. Full article

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. Full article

Marine sources contribute approximately 2% of global energy-related CO₂ emissions, with the shipping industry accounting for 87% of this total, making it the fifth-largest emitter globally. Environmental regulations by the International Maritime Organization (IMO), such as the MARPOL (International Convention for the Prevention of Pollution from Ships) treaty, have driven the exploration of alternative green energy solutions, including nuclear-powered ships. These ships offer advantages like long operational periods without refueling and increased cargo space, with around 200 reactors already in use on naval vessels worldwide. Among advanced reactor concepts, the molten salt reactor (MSR) is particularly suited for marine applications due to its inherent safety features, compact design, high energy density, and potential to mitigate nuclear waste and proliferation concerns. However, MSR systems face significant challenges, including tritium production, corrosion issues, and complex behavior of volatile fission products. Understanding the impact of marine-induced motion on the thermal–hydraulic behavior of MSRs is crucial, as it can lead to transient design basis accident scenarios. Furthermore, the adoption of MSR technology in the shipping industry requires overcoming regulatory hurdles and achieving global consensus on safety and environmental standards. This review assesses the current progress, challenges, and technological readiness of MSRs for marine applications, highlighting future research directions. The overall technology readiness level (TRL) of MSRs is currently at 3. Achieving TRL 6 is essential for progress, with individual components needing TRLs of 4–8 for a demonstration reactor. Community Readiness Levels (CRLs) must also be addressed, focusing on public acceptance, safety, sustainability, and alignment with decarbonization goals. Full article

The task of automatically and intelligently diagnosing faults in marine equipment is of great significance due to the numerous duties that shipboard professionals must handle. Incorporating automated and intelligent systems on ships allows for more efficient equipment monitoring and better decision-making. This approach has attracted considerable interest in both academia and industry because of its potential for economic savings and improved safety. Several fault diagnosis methods are documented in the literature, often involving mathematical and control theory models. However, due to the inherent complexity of some processes, not all characteristics are precisely known, making mathematical modeling highly challenging. As a result, fault diagnosis often depends on data or heuristic information. Fuzzy logic theory is particularly well suited for processing this type of information. Therefore, this paper employs fuzzy models to diagnose faults in a marine pneumatic servo-actuated valve. The fuzzy models used in fault diagnosis are obtained from the data. These fuzzy models are identified for the normal operation of the marine pneumatic servo-actuated valve, and for each fault, predicting the system’s outputs from the inputs and outputs of the process. The proposed fault diagnosis framework analyzes the discrepancy signals between the outputs of the fuzzy models and the actual process outputs. These discrepancies, known as residuals, help in detecting and isolating equipment faults. The fault isolation process uses an intelligent decision-making approach to determine the specific fault in the system. This method is applied to diagnose abrupt faults in a marine pneumatic servo-actuated valve. The approach presented was used to detect and diagnose three very important faults in the operation of a marine pneumatic servo-actuated valve. The three faults were correctly detected and isolated, and no errors were detected in this detection and isolation process. Full article

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. This paper presents a novel application of artificial intelligence tools for real-time posture estimation specifically designed for cruise machinists. The primary aim is to enhance occupational health and safety by providing precise, real-time feedback on ergonomic practices. We developed a dataset by capturing video recordings of cruise machinists at work, which were processed to extract skeletal outlines using advanced computer vision techniques. This dataset was used to train deep neural networks, optimizing them for accuracy in diverse and constrained shipboard environments. The networks were tested across various computational platforms to ensure robustness and adaptability. The AI model demonstrated high efficacy in recognizing both correct and incorrect postures under real-world conditions aboard ships. The system significantly outperformed traditional ergonomic assessment tools in terms of speed, accuracy, and the ability to provide instant feedback. The findings suggest that AI-enhanced ergonomic assessments could be a transformative approach for occupational health across various industries. Full article

As a subdivision of the hydrogen energy application field, ship-borne hydrogen fuel cell systems have certain differences from vehicle or other application scenarios in terms of their structural type, safety, environmental adaptability, and test verification. The connection method of the ship-borne hydrogen storage cylinder (SHSC) is very important for the hydrogen fuel cell ship, and the structural parameters of the SHSC are particularly important in the hydrogen refueling process. To ensure the safe and reliable operation of the hydrogen-powered ship, research on the filling of the SHSC under different connection modes was carried out during refueling. In our study, a thermal flow physical model of the SHSC was established to research the hydrogen refueling process of the series and parallel SHSCs. The influence of series and parallel modes of the SHSCs on the hydrogen refueling process was explored, and the evolution law of the internal flow field, pressure, and temperature of series and parallel SHSCs under different filling parameters was analyzed by numerical simulation. Our results confirmed the superiority of the parallel modular approach in terms of thermal safety during refueling. The results can supply a technical basis for the future development of hydrogen refueling stations and ship-board hydrogenation control algorithms. Full article

This paper presents an active flow control of ship airwake over the deck to improve the safety of helicopter shipboard operations in various angles of wind over deck (WOD). Firstly, an integrated flight dynamics method coupled with ship airwake was developed to analyze the effect of ship airwake on the helicopter at various angles of WOD. Then, an active flow control strategy in various angles of WOD was investigated with the analysis of airwake distribution and the impact on helicopter trim flight. Finally, the effects of active blowing on airwake distribution, flight control inputs, control margins, and helicopter attitudes in trim were analyzed. The results indicate that the variation of the angles of WOD has a significant impact on flight controls and helicopter attitudes for helicopter hovering relative to ship motion. The nonuniformity of ship airwake can be effectively alleviated by applying active flow control. The variations of flight control inputs and helicopter attitude angles at trim states are reduced, which increases the control margins and contributes to enhancing the safety of helicopter shipboard operations. Full article

To conduct a precise shock assessment of marine diesel engines, a 200 t floating shock platform was utilized to simulate realistic testing conditions. The testing generated the acceleration time curve and the shock response spectrum for the diesel engine. According to the applicable standards, the spectral velocity was chosen as the evaluation index, and an evaluation of the longitudinal, transverse, and vertical shock environment of the diesel engine was conducted. The shock factor interpolation method was corrected using the confidence interval based on normal distribution, and the interpolated confidence interval of the shock factor was determined. The findings reveal that shock waves were identified as the primary external force, and it was found that the influence of bubble pulsation can be disregarded when assessing a floating shock platform. This paper proposes the use of normal-distribution-based shock factor confidence intervals, which can accurately predict multidirectional shock factors and offer improved shock safety compared to the traditional method of unidirectional shock factor interpolation. The results and methods obtained in this study can provide valuable guidance and assistance for predicting the shock environment of large shipboard machinery on significant floating shock platforms. Full article

The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks. Full article