Ship Bridge Research Articles

Multirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions

Multirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions

Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field

In order to ensure the safety of navigation in a bridge area, and based on the theory of the safety potential field, a method of ship safety assessment and early warning in an inland river bridge area is proposed. Firstly, the risk elements associated with ship collisions in a bridge area are classified. Secondly, these risks are quantified using the potential energy field, the boundary potential field and the behavioural field, and then the ship state under the influence of wind flow, predicted by the Kalman filter, is quantified using the kinetic energy field. Then, the above four potential energy fields are merged to obtain a superposition field, and the magnitude of the instantaneous risk in the bridge area is obtained based on its magnitude. Finally, the change of field strength values under different moments is used for early warning. The results of the simulation of a ship passing through the piers of the Baijusi Bridge show that the model can effectively quantify the risk of a ship–bridge collision in the inland bridge area and provide real-time warning of the risk of a ship–bridge collision in the bridge area, which is of great significance for improving the safety of the inland bridge area.

A Sensitivity Analysis of Ship–Bridge Spacing under the Coupling Effect of Turbulence and Ship Motion

A Sensitivity Analysis of Ship–Bridge Spacing under the Coupling Effect of Turbulence and Ship Motion

Research on High-Pressure Water Jet Interference for Collision Prevention of Waterway Viaduct Piers: Case Study of Guangzhou Lixinsha Bridge

In this paper, with the frequent occurrence of ship–bridge collision accidents as the context and the collision accident of the Lixinsha Bridge in China as the background, the scenario of a ship impacting a pier was simulated using ANSYS-FLUENT software, and the practical application possibility of the high-pressure water jet interference (HPWJI) anti-collision method was thoroughly investigated. Through the simulation analysis, the effectiveness of a high-pressure water jet with a total flow rate of 45 m3/s in altering the navigation direction of large-tonnage (2000 t) ships and avoiding obstacles was verified. Additionally, its impact on the stress of the ship steel plates and navigation status was also explored. It was found that, with reasonable layout and parameter adjustment, the high-pressure water jet technology could effectively intervene in the ship’s navigation trajectory while ensuring the structural safety of the ship, with minimal impact on the ship’s navigation stability and passenger comfort. Furthermore, the injection angle of the high-pressure water jet had a significant impact on the deflection and deceleration of the ship. Specifically, when the water jet impacted the ship along its forward direction, it could effectively increase the ship’s deceleration and deflection time, reducing the speed from 2.55 m/s to 1.7 m/s, a decrease of approximately 33%, significantly enhancing collision prevention effectiveness. This research provides important guidance for the practical application of high-pressure water jet collision prevention technology and is of great significance for improving the safety of waterway transportation.

A computer vision approach to estimate the localized sea state

This research presents a novel application of computer vision (CV) and deep learning methods for real-time sea state recognition, aiming to contribute to improving the operational safety and energy efficiency of seagoing vessels, key factors in meeting the legislative carbon reduction targets. Our work focuses on utilizing sea images in operational envelopes captured by a single stationary camera mounted on the ship bridge. The collected images are used to train a deep learning model to automatically recognize the state of the sea based on the Beaufort scale. To recognize the sea state, we used 4 state-of-the-art deep neural networks with different characteristics that proved useful in various computer vision tasks: Resnet-101, NASNet, MobileNet_v2, and Transformer ViT -b32. Furthermore, we have defined a unique large-scale dataset, collected over a broad range of sea conditions from an ocean-going vessel prepared for machine learning. We used the transfer learning approach to fine-tune the models on our dataset. The obtained results demonstrate the potential for this approach to complement traditional methods, particularly where in-situ measurements are unfeasible or interpolated weather buoy data is insufficiently accurate. This study sets the groundwork for further development of sea state classification models to address recognized gaps in maritime research and enable safer and more efficient maritime operations.

Ship Bridge OOW Activity Status Detection Using Wi-Fi Beamforming Feedback Information

Officers on Watch (OOWs) of the ship’s bridge play a vital role in maritime navigation safety, monitoring the ship’s navigational status, and ensuring maritime safety. The status of inactive watch officers, such as fatigued driving and negligence on lookout, is one of the main causes of accidents. Intelligent technology for real-time perception and state evaluation of ship OOWs significantly reduces accidents caused by human factors. The traditional computer vision method is difficult to adapt to the complex environment of a ship bridge, and carries strong privacy risks. With the development of Internet of Things technology, sensing technology based on ubiquitous Wi-Fi devices provides a new way to accurately monitor the status of ship OOWs. In this paper, we use commercial off-the-shelf (COTS) Wi-Fi devices to propose a ship driving activity state detection method based on beamforming feedback information (BFI). Using wireless sensing data to sense the number of OOWs and their driving behavior realizes low-cost and high-precision detection of the behavioral status of the ship’s bridge watchkeeper. Experiments were conducted in a ship-driving simulation laboratory and on a real-world Yangtze River cruise ship. The experimental results demonstrate that our proposed method achieves 92.4% and 98.1% accuracy for tracking active status and estimating the number of OOWs, respectively.

Bending energy absorption performance of composite fender piles with different winding angles

Abstract In recent years, there has been an increase in accidents involving vessels colliding with bridge piers. These ship–bridge collisions can result in tragic loss of life and severe damage to the bridge structure. To address this issue, a type of fender pile made of winding-formed glass fiber-reinforced polymer (GFRP) was proposed as a solution. In this article, three- and four-point bending tests were performed to compare and analyze the damage modes and load-carrying capacity of the fender piles at two different winding angles, namely 45° and 75°. Vertical impact test was simulated using ANSYS/LS-DYNA to verify finite element models. The results revealed variations in damage patterns and bending performance of GFRP piles under the two fiber winding angles. The simulation results suggest that GFRP fender piles can effectively increase the impact time of ship–bridge collisions and reduce the collision forces, thereby significantly improving the protection of bridge piers.

Bridge Duty On A Ship: Study On Preventing Collisions At Sea On The Training Ship John Lie

The knowledge and skills of a ship's crew can play a key role in preventing collisions. The Bridge Watchkeeping Duty becomes crucial in this context, as they are the first to detect potential threats. Given the increasing risks in maritime transportation and the need to prevent ship collisions, several steps can be taken. One of them is to effectively carry out the tasks and functions of the Bridge Watchkeeping Duty. Therefore, this study aims to analyze the Bridge Watchkeeping Duty, specifically, the efforts to prevent sea collisions conducted by the Ship's Bridge Watchkeeping Service, and how its theoretical and practical implementation is carried out in the field. This qualitative study uses a descriptive method to describe and elaborate on the study object, namely the John Lie training ship. The study will include quotations from various data sources such as interviews, field notes, memos, and other official documents. Data analysis in this study uses the techniques of Miles and Huberman. The results of this study indicate that the implementation of these duties and responsibilities must always be based on a deep understanding of various national and international maritime regulations, as well as knowledge of the characteristics of the ship and its surrounding environment. Additionally, implementing regulations such as the 2010 Manila Amendments to the STCW 2010 is also important to ensure the welfare and fitness of the watchkeepers so they can perform their duties and responsibilities optimally. This study has several limitations, such as variables that need to be explored further, such as the psychological aspects of bridge watchkeepers who bear a heavy workload, extreme sea weather conditions, and ship facilities that support collision prevention activities. Thus, this study is expected to provide constructive thoughts and suggestions for maritime transportation stakeholders, particularly sailors and crew members, regarding the importance of properly implementing the Bridge Watchkeeping Duty. Focus on the human aspect in preventing ship collisions at sea through the Ship's Bridge Watchkeeping Service.

Application of a text mining method in navigation and communication for enhancing maritime safety.

This paper introduces a model for the translation of natural language into ontology and vice versa in an autonomous navigation system of a sea-going vessel. The system comprehensively executes communication tasks at sea. The authors use machine learning methods in the field of text mining and basic and additional properties of ontologies. The newly developed ontology is applicable in shipping. The key elements of the prototype are the sequence of communication commands given from the ship's bridge, decomposition, extraction of the communication sequence and the rule base. The presented model has been implemented and verified in selected scenarios of collision situations at sea. The test results confirm that the assumptions, the designed system architecture and the algorithms in the prototype are correct.

From integrated bridge system to marine bridge domain: A computational perspective

With the progressive advancement of ship intelligence, there has been a corresponding augmentation in the sensory, control, and other equipment within ship bridge systems. The accelerated expansion of both hardware and software components in ships poses formidable challenges to the existing bridge systems. The prevalent deployment format of ship bridge systems adheres to a traditional decentralized structure, limiting the system's computational prowess, equipment compatibility, and data fusion capabilities. Addressing these constraints and augmenting the ship bridge system's information processing, equipment compatibility, and functional integration represent pivotal objectives. Thus, this paper embarks on an analysis of the developmental trajectory, challenges, and predominant trends characterizing the current Integrated Bridge System (IBS) in ships. Subsequently, harnessing the inherent advantages of software and hardware decoupling, centralized computational capabilities, and seamless integration of data and functions, we propose a comprehensive maritime bridge domain architecture. This proposed architecture is meticulously delineated, encompassing aspects such as overall scheme design, hardware and software subsystems, and the mechanism of data service. In conclusion, the paper advocates a systematic approach to system development, encompassing implementation, testing, and evaluation of the proposed architecture. Rigorous testing and evaluation of the architecture, focusing on data collection, network transmission performance, functional availability, data distribution efficiency, and task processing capabilities, affirm its marked enhancements in computational proficiency, data service efficiency, and functional integration within the IBS. These results lay a robust foundation for the intelligent design of ship systems, paving the way for the evolution of intelligent ships.

Wi-Watch: Wi-Fi-Based Vigilant-Activity Recognition for Ship Bridge Watchkeeping Officers

Wi-Watch: Wi-Fi-Based Vigilant-Activity Recognition for Ship Bridge Watchkeeping Officers

Study on risk of ship collision in bridge life-cycle based on synergetic theory

Studies on the risk of ship–bridge collisions have always been a significant subject in academic research. However, the study of ship–bridge collision risk is rarely mentioned from the perspective of the bridge life-cycle. This study proposes the concept of “bridge–ship common safety” based on the synergetic theory and constructed a high-level cooperative platform to solve the problem of bridge–ship collisions. This work analyzed the interaction between the subsystems of environment, ship and bridge. In this paper, a method for analyzing the ship–bridge collision risk is proposed based on synergetic theory with order parameters, including ship–bridge collision probability and the bridge's collapse probability. The Lanjiang Xiangnv Bridge project was taken as the background for this study. The bridge risk was evaluated by utilizing order parameters and synergetic degrees. The results show that the model can reflect the risk of ship–bridge collision, with a high level of scientific significance and academic value, enriching the bridge-collision prevention theory and implementing the concept of "bridge–ship common safety".

Risk analysis of bridge ship collision based on AIS data model and nonlinear finite element

Abstract To solve the problem of calculating the probability of ship collision in ship bridge collision risk assessment, the impact parameters of ship collision are obtained based on the automatic identification system (AIS) data to solve the problem that the existing methods do not consider the actual navigable ship information in a specific bridge navigation area. Based on AIS data, the dynamics of sailing ships are analyzed, parameters such as ship position, speed, and yaw angle, are obtained, ship traffic flow is analyzed, and the geometric probability in the actual ship traffic flow specification model is modified. The results before and after correction were compared and analyzed. The results show that the maximum transverse displacement of the anti-collision device is about 1.5 s under all working conditions, indicating that the collision force drops continuously from this moment to 0 in the collision force time history. In the process of collision, the anti-collision device absorbs part of the collision energy through its own deformation. Under the premise of a certain initial kinetic energy, the deformation caused by the collision energy absorption after fortification will be reduced. The anti-collision device has local permanent deformation under the impact of 5,000 t ship, but no damage and failure, and will not cause water entry and subsidence. It proves that the constructed fishery ship collision risk assessment model and the developed fishery ship safety management and evaluation system are reliable and the prediction results are credible, which can provide scientific methods for the safety management and evaluation of fishery vessels. For the bridge area with complicated ship navigation conditions, it is necessary to use the actual navigable ship information obtained based on AIS data to estimate the distribution of ships in the bridge area to improve the accuracy of the calculation results.

BridgeInsight: An asset profiler for penetration testing in a heterogeneous maritime bridge environment

A maritime bridge environment is a heterogeneous ecosystem of complex systems for various operations. As part of new requirements set by the International Association of Classification Societies, ship operators must now maintain an asset inventory aboard vessels specifically to improve their cyber security. This paper discusses the development of a ship-specific asset profiler that will not only identify and record the devices present automatically but also provide an in-depth analysis of their properties and characteristics in an intelligent and user-friendly manner. As cyberattacks increase in the maritime industry, proper testing of ship systems is essential, to ensure vessels remain secure and the risk of a cyberattack is minimized. An asset profiler for the bridge environment would serve as a tool for profiling the devices, helping personnel make faster and well-informed decisions, and could be a component of a wider audit framework. This paper presents a ship bridge profiler (i.e., BridgeInsight) used to identify all devices on the bridge of a vessel automatically and which provides information on them using a generated PDF report that consists of graphs and charts. To do this, it uses the Random Forest classifier algorithm, and the information it provides will enable the auditor or pen tester to perform manual testing or automate audits, while also providing comprehensive information that engineers and mariners can use to comply with regulations. Highlights As part of new requirements set by the International Association of Classification Societies, ship operators must now maintain asset inventory aboard vessels specifically to improve their cyber security. This paper presents a ship bridge profiler (i.e., BridgeInsight) used to identify all devices on the bridge of a vessel automatically. We envision automated asset detection and classification to have even more benefit in future cyber security work, as penetration testing.

A full-time deep learning-based alert approach for bridge–ship collision using visible spectrum and thermal infrared cameras

With the rapid development of transportation infrastructure and the growing water transportation industry, many bridges have been constructed across navigable rivers. However, collisions between ships and bridges remain a frequent occurrence, particularly under low illumination conditions. To address this issue, this paper proposes a full-time bridge–ship collision alert approach based on the combination of visible spectrum and infrared cameras. This approach includes the following steps: (1) capturing and identifying ships in the channel using a single visible spectrum camera is challenging in low illumination environments such as nighttime. To overcome this limitation, we propose the use of infrared thermal imaging technology, which can monitor ships at night due to their inherent emissivity of ship thermal radiation. By comprehensively analyzing factors such as the real-time lighting environment and comparing the advantages of visible spectrum cameras and thermal infrared cameras in object recognition under various illuminance levels, we determine the effectiveness of the decision-making method of the dual-channel camera for ships under different illumination conditions. (2) To detect and track ships in the channel, a deep learning-based automatic detection and tracking method is designed using visible spectrum and infrared thermal imaging cameras, providing early warning of dangerous trajectories of ships that threaten the piers. To address the real-time identification and positioning of ships by dual-channel cameras in different light environments, we adopt a target detection network and a displacement calculation algorithm for multi-target tracking. Analysis results demonstrate that this method can automatically assign visible spectrum and thermal imaging cameras as effective input sources in different illumination environments to detect and warn ships with dangerous trajectories, contributing to the safety of navigable ships and bridges in low illumination conditions.

Application of virtual reality as an alternative training media as complement of ship bridge simulator (case study: port of tanjung emas semarang)

The application of computer technology such as 3D modeling, simulators, virtual reality, and augmented reality, will always provide benefits to the maritime sector. The object of study used in this research is the physical bridge simulator at Poltekpel Surabaya which is used as a virtual simulator using virtual reality. Through the application of virtual reality technology, it can be developed into an educational facility for the cadets there which does not only rely on 2D or 3D forms but can be used as a simulator where users can interact directly in the application. With this simulator, it can save in terms of manufacturing costs and in terms of usage it becomes more effective and practical. In this simulator application the user can participate in a virtual 3D environment that is simulated like a real environment. With a simulator using virtual reality, users can learn how to operate a ship like a captain without having to visit the ship’s bridge directly. Simulators with virtual reality will provide a virtual learning environment that can be accessed easily anywhere and anytime. Based on the results of the cadet assessment test questionnaire consisting of 7 questions with 30 respondents, it can be concluded that the VR application for the Bridge Simulator is in the very good category because the application is easy to understand by the respondents and the 3D results from the ship’s bridge at Poltekpel are the same with those in the simulator application and for the results of the questionnaire calculations have a total value of 908 which is between 840 to 1050, or 87% in the percentage calculation.

Implementation of Metaverse for Modeling The Ship Bridge Simulator Based on Virtual Reality As Educational Purposes, Case Study: Tanjung Priok Port Area

The development of virtual reality technology has increased in recent years with the characteristics and benefits of virtual reality that can help solve complex problems that exist in the real world. Virtual reality technology has been widely applied as a means of education. The ship bridge simulator is one of the supporting tools for training shipping cadets, but this tool has a relatively high cost. Therefore, this research aims to model the ship bridge simulator. The initial stage in this research is to model the ship bridge simulator belonging to the Surabaya Shipping Polytechnic (Politeknik Pelayaran Surabaya) with a case study of Tanjung Priok Port, referred to as the metaverse area. The process is to convert real objects into 3D assets using Blender software and develop them into a virtual reality application package designed using Unity software. The results obtained at the initial stage of this research are to provide a virtual forward and backward feature of the ship in the metaverse area. This virtual reality application has achieved its goals based on user ratings involving about 30 respondents. It is evidenced by its rating score of 908 out of 1050, which shows that this virtual reality application is included in the excellent category.

Whereables? Examining Personal Technology Adoption in Contemporary Control Rooms

Early work in wearables research has often proposed visions in which wearable computers are introduced to support human operators in critical environments such as control rooms, ship bridges, cockpits, or operating rooms. Wearable assistants could for instance present critical task-relevant information to users regardless of their location, help in avoiding procedural errors, or enhance collaborations between multiple operators. In reality, however, such visions have not galvanized: What happened? And could operators’ attitudes and misgivings toward wearables be responsible? The rise of personal wearables in the past years has led to fitness trackers, smartwatches, and other consumer devices being worn by a larger audience, likely also among control room operators. We report in this article on findings from a series of onsite interviews and workshops with professional control room operators to get an insight in their attitude toward wearables, and opinions and current views on the use and adoption of wearable and pervasive technologies in their work environment.

Incorporation of seafarer psychological factors into maritime safety assessment

Psychological factors have been a critical cause of human errors in sectors such as health and aviation. However, there is little relevant research in the maritime industry, even though human errors significantly contribute to shipping accidents. It becomes even more worrisome given that seafarers are changing their roles onboard ships due to the growth of automation techniques in the sector. This research pioneers a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. It quantitatively enables the psychological factor assessment and hence can be used to test, verify, and train seafarers' behaviours for ship safety at sea and along coasts. A case study on ship collision avoidance in coastal waters demonstrates its feasibility using ship bridge simulation. An experimental framework incorporating neurophysiological data can be utilised to effectively evaluate the contribution of psychological factors to human behaviours and operational risks. Hence, it opens a new paradigm for human reliability analysis in a maritime setting. This framework provides insights for reforming and evaluating operators’ behaviours on traditionally crewed ships and in remote-controlled centres within the context of autonomous ships. As a result, it will significantly improve maritime safety and prevention of catastrophic accidents that endanger oceans and coasts.

A discomfort threshold for impulsive whole-body vibration on a slamming-prone vessel

This study contributes towards a threshold for the onset of discomfort stemming from vibration that is transmitted to the human body by means of impulsive wave slamming in dynamic shipping environments. Wave slamming is a random, violent, non-linear event. Subjective and objective data were collected from two research voyages on a slamming-prone vessel. Full-scale vertical acceleration measurements were conducted near work and accommodation areas on the vessel. A daily diary survey was used to gather human responses among passengers. In addition, instantaneous slamming vibration comfort ratings were obtained by observers on the ship Bridge during a test sequence that purposefully induced slamming. The results indicate that two different approaches converged to the same VDV1hr comfort threshold (0.41 m/s1.75 and 0.43 m/s1.75), which corresponds to the limit where 50% of respondents indicated discomfort. A similar analysis using the r.m.s. metric converged to an identical threshold (0.03 m/s2), irrespective of the accumulated time of assessment.