Actual Ship Research Articles

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships.

Experimental and Numerical Investigation of the Buckling Behavior and Strength of Combined Opening Plate Girders in Passenger Ships

High-stiffener-web combined opening girders used on passenger ships are prone to plastic hinge failures around the opening area and overall instability under combined and vertical loads, exhibiting complex buckling behaviors. In response to such situations, a series of numerical simulations and experiments on combined opening girders were conducted, considering several affecting factors such as opening shapes, initial crack defects, strengthening measures and stiffener web dimensions. On the basis of the verification of the reliability of the numerical method, the load-bearing characteristics of the combined open plate girders were investigated. It is concluded that the lumbar round opening can lead to localized plastic hinge failure phenomena, complicating the buckling behavior. In contrast, the inclusion of stiffeners can significantly improve the load-bearing capacity after the point at which instability occurs in the original specimen. In addition, detailed relationships between deformation trends and external loads are illustrated, which can be used as a reference for the optimal design of combined opening plate girders in actual ship structures.

The Risk Analysis of Chemical Tankers Passing Through the Turkish Straits between 2010 – 2022

This analysis aims to highlight the risk created by the chemical tankers by comparing their movements in the Turkish straits. This information is based on actual ship movements from south to north and the return voyage, showing the prominence of chemical tankers passing through the straits between 2010 and 2022 and underlining the importance of the Turkish Straits. Istanbul Bosporus transit passages are being regulated by the Montreux Convention1, signed 84 years ago. As per the convention, international shipping provided safe and seaworthy ships, has the right of free passage through the Straits in peacetime. Since the convention has been in force, the volume and frequency of the ships have increased almost tenfold, an interesting correlation on average, there were13 passages in a day in 1938; which increased to 65 by1985 and 115 by 2019. The main reason for this increase is said to be the hydrocarbon reserves of the Caspian Region2. As the number of passages increased enormously, so did the physical risk to shipping and the environmental and biological risk to local species. In order to minimise any possible risks and provide safer and environmentally friendly traffic, Turkish authorities have had to adopt Maritime Traffic Regulations for the Turkish Straits on 15/08/2019 (VTS – Vessel Traffic System)3.

An experimental investigation of the effect of ride control systems on the motions response of high-speed catamarans in irregular waves

To ensure high-quality passenger comfort and enhance the operability and performance of high-speed catamarans, reducing the severity of motions and structural loads using Ride Control Systems (RCS) is beneficial. To optimize ride control algorithms, it is essential to have a thorough understanding of the RCS in actual ship sailing conditions. In this paper, a set of towing tank tests was undertaken to study the effectiveness of different control algorithms, including linear and nonlinear versions of the heave control, pitch control, and local control, on motion responses of a 2.5 m scaled model of a 112 m INCAT Tasmania high-speed catamaran in irregular waves. The RCS included a centre bow-fitted T- Foil and two transom-mounted stern tabs. Obtained results were also compared with the scale model responses with passive RCS and with no RCS fitted. Heave and pitch motion responses as well as vertical accelerations of the catamaran were calculated in head seas at a model speed of 2.89 m/s (37 knot full scale), a modal period of 1.5 s (10 s full scale) and two significant wave heights of 60 mm and 90 mm, simulating full scale wave heights of 2.7 m and 4 m, respectively. It was demonstrated that deploying a passive RCS led to modest reductions in the peak motion responses. The most significant reductions in ship motions took place in the nonlinear modes of the heave and pitch control algorithms at a significant wave height of 60 mm (full-scale 2.7 m). In this condition, the nonlinear pitch control mode was demonstrated to be the most effective algorithm since this control mode mitigated the peak pitch RAO by 41% and vertical accelerations by 46%. This was a substantial reduction in ship motions with the RCS at an equivalent full-scale speed of 37 knot at 2.7 m wave height in the random sea condition.

Impact of Hull Fouling on Vessel’s Fuel Consumption and Emissions Based on a Simulation Model

With an ever-increasing trend of analysing and improving vessel energy and economic efficiency in recent years and decades, every aspect of a vessel’s system needs to be observed with the goal of reducing fuel consumption and emissions. Hull fouling can have a significant effect on these variables. Since hull maintenance is an expensive effort, its use must be optimized and fine-tuned to increase the economic efficiency of a vessel’s exploitation cycle. In order to do this, data and a subsequent analysis have to be obtained on different stages of the hull fouling process and the effect these states have on vessel energy efficiency and consequently emissions and economic efficiency. This paper will analyse a set of data including emission pollutants such as nitrogen oxides (NOX) and carbon dioxide (CO2) as a greenhouse gas and the effect that different amounts of hull fouling have on the vessel’s fuel consumption and emissions under different propulsion loads. The aforementioned data is obtained from a simulation model of a RoPax vessel. The advantage of using data from a simulation model of a RoPax vessel for the research discussed in this paper is the ability to analyse various conditions not easily reproduced on actual ships. Main research findings presented in this paper are consistent in proving that increased hull fouling leads to increased fuel consumption and emissions as high as 15% increase in extreme cases. Keywords: hull fouling, fuel consumption, pollutant, greenhouse gas, emissions, simulator

Enhancement method of series hybrid ship energy efficiency for speed and energy collaborative optimization

AbstractHybrid ships have recently garnered increasing attention as a promising solution for energy shortages and environmental pollution. However, changes in the navigation environment can greatly affect the energy efficiency of these vessels. Therefore, improving the energy efficiency of hybrid ships continues to be a key issue. Our study examined the energy efficiency of a diesel‐electric series hybrid ship operating on an inland river, and a method to optimise series hybrid ship energy efficiency was proposed. The energy consumption model, power demand model, and decision function with minimum fuel consumption as the evaluation criteria were established according to the ship's characteristics and hybrid system. By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. Afterwards, the optimised speed of the ship and the optimised output power of each power source were determined using the grasshopper optimization algorithm. The ship's energy efficiency levels before and after using the energy efficiency enhancement method were compared and analysed, and the effects of this optimization method on the ship's energy efficiency in several typical voyages were analysed. Our findings demonstrated that this optimization method can distribute the output of the power source in a better way, thereby optimising the speed of the vessel and maintaining stable sailing. More importantly, the proposed method can reduce fuel consumption by 17.04%.

Lean six sigma methodology for waste reduction in ship production

This study was conducted to reduce the amount of waste in ship production and assembly processes at PT ES. Several wastes during the ship production process result in the actual ship completion time being longer than the planning time set by the process planner. Therefore, accurate analysis is required to reduce waste. In this way, contributing factors can be identified, and more effective solutions can be obtained to reduce waste. This is done by implementing the Lean Six Sigma method (DMAIC processes) and several tools and methods, such as Pareto and fishbone diagrams and the FMEA method. The results show that the most critical potential root cause affecting production delays comes from the potential causes with the highest Risk Priority Number (RPN) value. The causes are welders who do not understand the WPS (RPN 432), unstable welding transformers (RPN 432), and unproductive loader movements (RPN 384). The recapitulation of welding defects produced in the production process at a sigma level of 2.48. Recommendations for the three potential critical wastes were made and planned for implementation. The estimated average RPN impairment for the three critical root causes was 32.3%. This condition will impact the total ship production time, which is 6% shorter (equivalent to 14 days) than the previous production time with a new sigma level of 2.55.

Development of an Automated Spare-Part Management Device for Ship Controlled by Raspberry-Pi Microcomputer Based on Image-Progressing & Transfer-Learning

As the development of autonomous ships is underway in the maritime industry, the automation of ship spare part management has become an important issue. However, there has been little development of dedicated devices or applications for ships. This study aims to develop a Raspberry Pi-based embedded application that identifies the type and quantity of spare parts using a transfer learning model and image processing algorithm suitable for ship spare part recognition. A newly improved image processing algorithm was used to select a transfer learning model that balances accuracy and training speed through training and validation on a real spare parts dataset, achieving a prediction accuracy of 98.2% and a training time of 158 s. The experimental device utilizing this model used a camera to identify the type and quantity of spare parts on an actual ship. It displayed the spare parts list on a remotely connected computer. The ASSM (Automated Ship Spare-Part Management) device utilizing image processing and transfer learning is a new technology that successfully automates spare part management.

A Pilot Study of Stacked Autoencoders for Ship Mode Classification

With the evolution of the shipping market, artificial intelligence research using ship data is being actively conducted. Smart ships and reducing ship greenhouse gas emissions are among the most actively researched topics in the maritime transport industry. Owing to the massive advances in information and communications technology, the internet of things, and big data technologies, smart ships have emerged as a very promising proposition. Numerous methodologies and network architectures can smoothly collect data from ships that are currently in operation, as is currently done in research on reducing ship fuel consumption by deep learning or conventional methods. Many extensive studies of stacked autoencoders have been carried out in the past few years. However, prior studies have not addressed the development of algorithms or deep learning-based models to classify the operating states of ships. In this paper, we propose for the first time a deep learning-based stacked autoencoder model that can classify the operating state of a ship broadly into the categories of At Sea, Stand By, and In Port, using actual ship power load data. In order to maximize the model’s performance, the stacked autoencoder architecture, number of hidden layers, and number of neurons contained in each layer were measured by performance metrics such as true positive rate, false positive rate, Matthews correlation coefficient, and accuracy. It was found that the model’s performance was not always improved by increasing its complexity, so the feasibility of developing and utilizing an efficient model was verified by comparing it to real data. The best-performing model had a (5–128) structure with latent layer size 9. It achieved a true positive rate of 0.9035, a false positive rate of 0.0541, a Matthews correlation coefficient of 0.9054, and an accuracy of 0.9612, clearly demonstrating that deep learning can be used to analyze ship operating modes.

대형 비행갑판을 갖는 함정과 풍동시험 결과를 활용한 고신뢰도 함정 Airwake 예측

Developing high-fidelity Computational Fluid Dynamics (CFD) simulation methods used to evaluate the airwake characteristics along a flight deck of a large ship, the various kind of data such as actual ship measurement and wind tunnel results are required to verify the accuracy of CFD simulation. Inflow velocity profile at the bow, local unsteady flow field data around the flight deck, and highly reliable wind tunnel data which were measured after reviewing Atmospheric Boundary Layer (ABL) simulation and Reynolds Number effects were also used to determine the key parameters such as turbulence model, time resolution and accuracy, grid resolution and type, inflow condition, domain size, simulation length, and so on in STAR CCM+. Velocity ratio and turbulent intensity difference between Full-scale CFD and actual ship measurement at the measurement points show less than 2% and 1.7% respectively. And differences in velocity ratio and turbulence intensity between wind tunnel test and small-scale CFD are both less than 2.2%. Based upon this fact, the selected parameters in CFD simulation are highly reliable for a specific wind condition.

Ship Anomalous Behavior Detection Using Clustering and Deep Recurrent Neural Network

In this study, we propose a real-time ship anomaly detection method driven by Automatic Identification System (AIS) data. The method uses ship trajectory clustering classes as a normal model and a deep learning algorithm as an anomaly detection tool. The method is divided into three main steps: (1) quality maintenance of the original AIS data, (2) extraction of normal ship trajectory clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which a segmented improved Dynamic Time Warping (DTW) algorithm is used to measure the degree of trajectory similarity, (3) the clustering results are used as a normative model to train a Bi-directional Gated Recurrent Unit (BiGRU) recurrent neural network, which is used as a trajectory predictor to achieve real-time ship anomaly detection. Experiments were conducted using real AIS data from the port of Tianjin, China. The experimental results are manifold. Firstly, the data pre-processing process effectively improves the quality of raw AIS data. Secondly, the ship trajectory clustering model can accurately classify the traffic flow of different modes in the sea area. Moreover, the trajectory prediction result of the BiGRU model has the smallest error with the actual ship trajectory and has a better trajectory prediction performance compared with the Long Short-Term Memory Network model (LSTM) and Gated Recurrent Unit (GRU). In the final anomaly detection experiment, the detection accuracy and timeliness of the BiGRU model are also higher than LSTM and GRU. Therefore, the proposed method can achieve effective and timely detection of ship anomalous behaviors in terms of position, heading and speed during ship navigation, which provides insight to enhance the intelligence of marine traffic supervision and improve the safety of marine navigation.

Novel methods for reliability study of multi-dimensional non-linear dynamic systems

This research presents two unique techniques for engineering system reliability analysis of multi-dimensional non-linear dynamic structures. First, the structural reliability technique works best for multi-dimensional structural responses that have been either numerically simulated or measured over a long enough length to produce an ergodic time series. Second, a novel extreme value prediction method that can be used in various engineering applications is proposed. In contrast to those currently used in engineering reliability methodologies, the novel method is easy to use, and even a limited amount of data can still be used to obtain robust system failure estimates. As demonstrated in this work, proposed methods also provide accurate confidence bands for system failure levels in the case of real-life measured structural response. Additionally, traditional reliability approaches that deal with time series do not have the benefit of being able to handle a system's high dimensionality and cross-correlation across several dimensions readily. Container ship that experiences significant deck panel pressures and high roll angles when travelling in bad weather was selected as the example for this study. The main concern for ship transportation is the potential loss of cargo owing to violent movements. Simulating such a situation is difficult since waves and ship motions are non-stationary and complicatedly non-linear. Extreme movements greatly enhance the role of nonlinearities, activating effects of second and higher order. Furthermore, laboratory testing may also be called into doubt due to the scale and the choice of the sea state. Therefore, data collected from actual ships during difficult weather journeys offer a unique perspective on the statistics of ship movements. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information about the extreme response from available on-board measured time histories. Both suggested methods can be used in combination, making them attractive and ready to use for engineers. Methods proposed in this paper open up possibilities to predict simply yet efficiently system failure probability for non-linear multi-dimensional dynamic structure.

Comparison of imaging techniques for the analysis of bubble distribution in ship wakes

Ship wakes, which are inevitably caused by ship movement, are a subject of great interest for military purposes. In this study, we created an underwater acoustic signal measuring device to characterize the distribution of wakes from a ship maneuvering in a real ocean environment. Two sets of multi-array sonar systems consisting of multiple projectors and multiple hydrophones were fabricated to acquire underwater acoustic signals. The systems were then placed vertically at a certain distance from each other in the sea. Afterward, we stably acquired the transmitted and scattered signals from the bubble wake generated by an actual ship. The distribution of bubbles in the ship's wake was then imaged by applying the RTM (reverse-time migration) and WI (waveform inversion) numerical analysis techniques to image the bubble distribution in the ship wake. Finally, the results obtained using the two aforementioned techniques were analyzed and compared.

A cellular automata ship traffic flow model considering navigation rules in narrowing channel

To present an analysis and application method of ship traffic flow considering navigation rules in narrowing channel, which can make a reasonable judgment for decision makers to improve navigation efficiency and ensure navigation safety. In this paper, the relationship model between narrowing channel and ships is analyzed from the perspective of navigation. Based on the constraint condition of navigation safety domain, the navigation rules are formulated for different positions of channel, and the mutual influence of ships at the narrowed position of channel is analyzed emphatically. The establishment scheme of traffic flow model of narrowing channel with navigation safety distance is put forward by using cellular automata algorithm. According to the measured data, the correctness of the ship traffic flow model of narrowing channel is verified. The experimental results show that the relative error rates are between −2.29% and 10.89%, which indicating that the method can better conform to the actual ship traffic flow characteristics of channel narrowing and has strong application value.

An INS-aided MASS autonomous navigation algorithm considering virtual motion constraints and the leeway and drift angle

Autonomous navigation is one of the critical enabling technologies in developing MASS or intelligent ships. This study proposes an autonomous navigation algorithm with INS-aided shipborne GNSS. The proposed algorithm constructs the ship's virtual motion attitude by considering the leeway and drift angle, and takes into account the one-dimensional speed constraints in the virtual ship body reference frame. This approach addresses the issue that the conventional INS and shipborne GNSS integrated navigation scheme can utilize the three-dimensional position information only, without considering the measurement update from the one-dimensional speed obtained by shipborne GNSS. The algorithm is verified by actual ship departure and cruising experiments in natural marine environments. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm when the GNSS speed and position information are available. In the scenario when the GNSS position information is temporarily lost, the proposed algorithm can continue to derive relatively reliable navigation parameters for the safe navigation of ships with only one-dimensional speed measurement.

Recognition of Unsafe Onboard Mooring and Unmooring Operation Behavior Based on Improved YOLO-v4 Algorithm

In the maritime industry, unsafe behaviors exhibited by crew members are a significant factor contributing to shipping and occupational accidents. Among these behaviors, unsafe operation of mooring lines is particularly prone to causing severe accidents. Video-based monitoring has been demonstrated as an effective means of detecting these unsafe behaviors in real time and providing early warning to crew members. To this end, this paper presents a dataset comprising videos of unsafe mooring line operations by crew members on the M.V. YuKun. Additionally, we propose an unsafe behavior recognition model based on the improved You Only Look Once (YOLO)-v4 network. Experimental results indicate that the proposed model, when compared to other models such as the original YOLO-v4 and YOLO-v3, demonstrates a significant improvement in recognition speed by approximately 35% while maintaining accuracy. Additionally, it also results in a reduction in computation burden. Furthermore, the proposed model was successfully applied to an actual ship test, which further verifies its effectiveness in recognizing unsafe mooring operation behaviors. Results of the actual ship test highlight that the proposed model’s recognition accuracy is on par with that of the original YOLO-v4 network but shows an improvement in processing speed by 50% and a reduction in processing complexity by about 96%. Hence, this work demonstrates that the proposed dataset and improved YOLO-v4 network can effectively detect unsafe mooring operation behaviors and potentially enhance the safety of marine operations.

Flexible guided anti-collision device for bridge pier protection against ship collision: Numerical simulation and ship collision field test

The flexible guided anti-collision device (FGAD) can prolong the impact time between ship and pier, so that the ship has enough time and space to turn and deviate from the pier. In this way, the ship can retain most of the kinetic energy, and the impact force on the pier can be greatly reduced. Both of ship and pier can be protected by the FGAD. In this paper, the dynamic response and the energy-dissipation capacity of FGAD are firstly studied by the numerical simulation of ship-bridge collision process. The results show that the FGAD can effectively turn the ship's course, and the concentrated force of collision is transformed into the distributed force, which significantly reduces the energy conversion and impact force. Then, an actual ship collision field test is carried out to investigate the effectiveness of the FGAD for reducing the impact force on the pier and turning the navigation direction of ship. The test results show that the FGAD can turn the ship's course with a little change of the ship speed after the collision. Most of the kinetic energy does not participate in the energy exchange and the impact force on the pier is significantly reduced. The FGAD realizes the design idea of protecting both the bridge and the ship. The application of engineering also confirms the effectiveness and reliability of the FGAD.

Safe Mooring of Ships Under the Influence of Wind and Currents

In this paper, the simplified calculation method in the "Load Code for harbor Engineering" to quickly calculate the force on moored ship's lines and wharf bollards under the influence of wind and currents is introduced. In view of the fact that real ship had happened mooring lines breakage accident at berth under ballast condition, use the actual ship X moored at NBGB wharf as an example to verify and calculate the force on ship’s lines and wharf bollards when the ship suffers the most adverse influence. The result show that when the ship encounters a gale (class 8) and a current (velocity 2.6 knot) in ballast condition, some of the mooring lines are subjected to forces close to their breaking strength under the combined effect of wind and current. If there is uneven force on the mooring lines, the possibility of mooring line breakage is great, which is consistent with the actual situation. By calculating and analyzing, give the ship’s mooring stabilize conditions under specific environment. The calculation method in this paper facilitates the captain to judge whether the ship can be safely and steadily moored, and facilitates the port to make clear regulations on the ship’s mooring stabilize according to the actual situation of the berth, which provides important theoretical support for the decision of stability and safety of the moored ship, the proposed preventive measures for the safety risks of stability and safety have reference value for improving the safe operation efficiency for the ship and the terminal.

Improved Multitarget Tracking in the Presence of Port–Starboard Measurement Ambiguity Using the Bayes Factor

We consider an application where an unmanned underwater vehicle (UUV) equipped with an acoustic sensor seeks to estimate the location of surface ships using relative angle measurements to the ships. The estimation problem is challenging due to ships occasionally appearing and disappearing from the sensor's field of view. On occasion, poor geometry between the sensor and the ships, and port–starboard ambiguity that is inherent in the sensor contribute to the challenges in the estimation problem. The latter challenge arises because the sensor cannot distinguish between sound sources on its port and starboard side (port–starboard ambiguity). Therefore, every measurement is associated with two possible sound sources that map each relative angle to bearing projections on the port and starboard side of the UUV. We approach the problem of identifying the origin of sound sources (relative angle measurements) that are most likely to be of actual ships using Bayesian hypothesis testing. We propose an assignment method that uses the Bayes factor as the criteria to recursively associate measurements to the target that is most likely to be an actual ship and not a false target. The effectiveness of our approach is evaluated by comparing the performance of three multitarget tracking algorithms with and without our method. Performance is evaluated offline using sea-trial data captured by a UUV developed by the Naval Research Laboratory and Bluefin Robotics.

Cargo vessel coupled deck panel stresses reliability study

Classic reliability methods, dealing with time series, do not have the advantage of dealing efficiently with system high dimensionality and cross-correlation between different dimensions. This study validates a novel structural reliability method suitable for multi-dimensional structural responses versus a well-established bivariate statistical method. An example of this reliability study was a chosen container ship subjected to large deck panel stresses during sailing. The risk of losing containers due to extreme motions is the primary concern for ship transport. Due to the non-stationarity and complicated nonlinearities of both waves and ship motions, it is challenging to model such a phenomenon. In the case of extreme motions, the role of nonlinearities dramatically increases, activating effects of second and higher order. Moreover, laboratory tests may also be questioned. Therefore, data measured on actual ships during their voyages in harsh weather provides a unique insight into statistics of ship motions. This work aims to benchmark and validate state of the art method, which makes it possible to extract the necessary information about the extreme response from onboard measured time histories. The method proposed in this paper opens up the possibility of predicting simply and efficiently failure probability for the nonlinear multi-dimensional dynamic system as a whole.