Ship Operation Research Articles

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

Shore control center (SCC) operators of maritime autonomous surface ships (MASS) may be allocated with complex responsibilities within different degrees of autonomy (DoA), from remote control to remote supervision, and the role of human in-the-loop is significant. In the current research on MASS, however, the safety of interactions between humans and systems are not sufficiently considered. Hence, this paper proposes a system-theoretic approach to safety analysis for human-system collaboration. The novelty of the approach is the definition of operational contexts of MASS and the integration of a human cognitive model into the system theoretic process analysis (STPA), called STPA-Cog. The method is demonstrated in two case studies of MASS: (i) remote control mode (RCM) and (ii) remote supervision mode (RSM), focusing on two types of navigational accidents (i.e., collision and grounding). The analysis results are derived by comparing the human-related and technical-related causal scenarios in RCM and in RSM. The findings can assist in human-oriented design and operational planning for SCC of MASS. Further, the proposed approach also has the potential to be used and extended to systemic safety analysis in other intelligent transportation systems.

Joint Optimal Energy Management and Voyage Scheduling for Economic and Resilient Operation of All-Electric Ships Considering Safe Return

Marine power systems are isolated from external grids, making them more vulnerable than land-based power systems. When ships are sailing on the ocean, complicated maritime environment and electrical component failure pose a crucial challenge to a safe and resilient shipboard power system. Different from fixed terrestrial microgrids, all-electric ships (AES) are mobile transportation vessels, complying with the navigation regulation of safe return to port. Thus, the economic and resilient operation needs to be considered from both energy and transportation aspects. In this regard, this study proposes a two-stage coordinated optimization framework for an AES, taking into account the impact of navigation. In the first stage, the normal operation mode follows the pre-voyage plan, which jointly optimizes voyage and energy management to reduce the whole voyage operation costs and greenhouse gas emissions simultaneously. In the second stage, a resilience-oriented optimization is developed to defend against emergencies by rescheduling navigation speed and generation. Furthermore, resistance during the whole AES navigation is also considered. Numerical results demonstrate the necessity of resistance impact on the voyage and the efficiency of the proposed operation scheme in economic and resilient operation for AESs.

Reduction of CO2 Emissions from Offshore Combined Cycle Diesel Engine-Steam Turbine Power Plant Powered by Alternative Fuels

Abstract Diverse forms of environmental pollution arise with the introduction of materials or energy that exert adverse effects on human health, climate patterns, ecosystems, and beyond. Rigorous emission regulations for gases resulting from fuel combustion are being enforced by the European Union and the International Maritime Organization (IMO), directed at maritime sectors to mitigate emissions of SOx, NOx, and CO2. The IMO envisions the realisation of its 2050 targets through a suite of strategies encompassing deliberate reductions in vessel speed, enhanced ship operations, improved propulsion systems, and a transition towards low and zero-emission fuels such as LNG, methanol, hydrogen, and ammonia. While the majority of vessels currently depend on heavy fuel or low-sulphur fuel oil, novel designs integrating alternative fuels are gaining prominence. Technologies like exhaust gas purification systems, LNG, and methanol are being embraced to achieve minimised emissions. This study introduces the concept of a high-power combined ship system, composed of a primary main engine, a diesel engine, and a steam turbine system, harnessing the energy contained within the flue gases of the main combustion engine. Assumptions, constraints for calculations, and a thermodynamic evaluation of the combined cycle are outlined. Additionally, the study scrutinises the utilisation of alternative fuels for ship propulsion and their potential to curtail exhaust emissions, with a specific focus on reducing CO2 output.

High-Performance Megawatt-Scale MVDC Zonal Electrical Distribution System Based on Power Electronics Open System Interfaces

Integrated Power and Energy Systems (IPESs) will play a critical role in supporting future complex electric ship operations. In particular, flexibility is required to exploit shipboard energy sources and storages for multiple ship functions, supporting a highly dynamic electrical power use. To this aim, the Medium Voltage DC (MVDC) electrical power distribution has been conceived to provide requested power flexibility, along with other expected advantages in terms of functional integration and ship design. For complex and high-performance ship power systems, the Zonal Electrical Distribution System (ZEDS) concept has been proposed to maximize MVDC capabilities even under extremely demanding operational conditions. This paper presents a megawatt-scale test facility of a MVDC ZEDS developed and built in Trieste, Italy, based on power electronics open system interfaces. The demonstrator is described in its power and control design architectures and capabilities. In particular, a successful MW-scale test is presented, to show ZEDS capability in supporting large and fast load variations, while maintaining acceptable MVDC voltage tolerances.

Prediction of wave-induced ship motions based on integrated neural network system and spatiotemporal wave-field data

This study introduces an artificial neural network system for ship motion prediction in seaways. To consider the physical characteristics of wave-induced ship motions, neural networks based on a Long Short-Term Memory (LSTM) encoder and decoder, and a convolutional neural network (CNN) are integrated. The LSTM encoder computes the state vector representing the memory effects of motion-induced radiated waves based on past motion records, that is, a sequence-to-one model. In the LSTM decoder, the motion time series is predicted using the encoded initial state vector and foreseen information on the ocean wave field around a vessel, that is, a sequence-to-sequence model. In addition, a CNN is adopted to compress the wave data into a vector sequence. Particularly, the present CNN uses spatiotemporal wave-field data, not a wave signal at single location. To validate the proposed system, a database for training the integrated system was constructed using a physics-based seakeeping program for various sea states. By applying the trained model, deterministic predictions were performed for a new ocean environment, and the accuracy and reliability of the testing results are investigated according to the input data and neural network structures. From the simulation results, it was confirmed that the present encoder–decoder system can conduct ship motion forecasting by effectively considering the motion memory effects and wave excitations as in the ship hydrodynamic model. In addition, excitations and resulting motion responses by short-crested waves can be considered through CNN-based wave-field data processing. Finally, the present machine-learning model also showed the capability of extracting ship operation information (maneuvering quantities) from the given wave-field data.

A Numerical Study on the Hydrodynamic Performance of a Tanker in Bow Sea Conditions Depending on Restraint Conditions

The importance of accurate ship performance estimation is increasing for efficient ship operation. Ship performance has been evaluated through model tests in the past, but there are limitations in terms of facilities and costs. With the spread of high-performance computers, the method of evaluating the performance of a ship by numerical analysis, especially computational fluid dynamics (CFD), has become common. There have been many numerical studies on added resistance under various wave conditions for many years, showing a high reliability. Meanwhile, most of the studies were conducted under conditions where the degree of freedom (DOF) of the ship was limited due to computational complexity. In this study, we tried to compare the added resistance performance and fluid dynamics of S-VLCC with 6 DOFs in the regular wave conditions. One of the methods for utilizing the 6 DOFs is the soft-mooring system, which allows springs to be attached to the bow and stern to recover the non-restoring force of the hull. The second method considers the free-running condition. The virtual disk is used for the self-propulsion of the ship, and the rudder can be rotated to maintain its course. The propeller rotation speed and rudder angle are controlled through PID control. The bow wave (ψ = 180°) and oblique wave (ψ = 150°, 120°) conditions were considered, and various regular wave conditions from short to long wavelengths were regarded. The effects of restraint conditions on the added resistance and motion response amplitude operator (RAO), according to each wave condition, were compared. As a result, there was a difference in the roll motion for each restraint condition, and the y-direction force and yaw moment generated on the hull were compared to analyze the cause. In addition, we observed the change in flow characteristics by comparing the streamlines around the hull and the nominal wake on the propeller plane.

A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of the inherently complex and dynamic growth of the VTF time series, a new hierarchical methodology for VTF prediction is proposed. Firstly, the original VTF data is reconfigured as a three-dimensional tensor by a modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) tensor decomposition model. Secondly, the VTF matrix (hour ✕ day) of each week is decomposed into high- and low-frequency matrices using a Bidimensional Empirical Mode Decomposition (BEMD) model to address the non-stationary signals affecting prediction results. Thirdly, the self-similarities between VTF matrices of each week within the high-frequency tensor are utilised to rearrange the matrices as different one-dimensional time series to solve the weak mathematical regularity in the high-frequency matrix. Then, a Dynamic Time Warping (DTW) model is employed to identify grouped segments with high similarities to generate more suitable high-frequency tensors. The experimental results verify that the proposed methodology outperforms the state-of-the-art VTF prediction methods using real Automatic Identification System (AIS) datasets collected from two areas. The methodology can potentially optimise relation operations and manage vessel traffic, benefiting stakeholders such as port authorities, ship operators, and freight forwarders.

Влияние дефектов распылителя форсунки на расход топлива и контроль топливной аппаратуры на современном уровне

The basis of improving the vessel energy efficiency is economical operation of its power units - the main and auxiliary diesel engines, steam boilers. There has been described the smart control system for ship operation. The main parameters listed below are under constant control: fuel consumption of the main and auxiliary steam diesel engines, auxiliary steam boiler, operating time of the main and auxiliary diesel engines, auxiliary steam boiler; fuel level in consumable, settling and bunker fuel tanks; vessel speed, vessel position, weather conditions. The system includes flow meters, temperature, speed, power, and pressure sensors, GPS-station, digital converters, controllers, marine computer industrial design, monitors for parameters in the engine control room and on the bridge, special software for ships and offices. There has been determined the main goal of developing and assembling the system: to develop a reliable working tool for the crew and the main ashore office, to evaluate and control the quality of the vessel operation, to supplement the existing control methods with an improved and more understandable scheme of monitoring various parameters of the ship's operation, including fuel consumption. The results of developing and introducing the system help increase the actual energy efficiency of the vessel operation. An example of real-time fuel consumption control using the smart control system - ShipSmartView - is presented. The functionality of the system is quite wide. Graphs of the current fuel consumption which indicated the defect in the fuel equipment are given. Based on the data, overhaul of the fuel equipment was carried out and a malfunction of the nozzle - a crack - was defined. It has been proved that due to untimely detection of a malfunction, a loss of fuel was allowed.

Research on Second-Hand Sailboat Prices Based on Machine Learning

With the development of the international shipping market and changes in second-hand ship prices, the operation and trade of second-hand ships are quite active. To accurately evaluate the price of second-hand ships, it is particularly important to establish a pricing model for second-hand ships. The first part of this article uses X-GBoost and PCA to rank and reduce the importance of features, and obtains four principal components. Based on 5 indicators and their importance ratios, establish an optimized X-GBoost regression model for Sine Cosine Algorithm (SCA) to predict the price of second-hand ships. In order to further investigate the impact of regional characteristics on prices, local population density, per capita GDP, and cargo throughput were selected to further classify regional characteristics. A linear regression model was established to investigate the impact of population density, per capita GDP, and cargo throughput on regional error prices, and to explore the impact of various regional characteristics on error prices. Divide sailboats into three categories – low-end sailboats, mid-range sailboats, and high-end sailboats, and discuss the impact of regional characteristics on prices. The study found that there is a significant difference in the prices of low-end sailboats and mid-range sailboats between regions, indicating consistency in regional effects. Studying the pricing of second-hand sailboats can help to correctly select trading markets, develop marketing strategies, purchase ships with higher economic priority, and promote the development and prosperity of the sailing industry.

Multi-agent system-based polymorphic distributed energy management for ships entering and leaving ports considering computing power resources

In order to realize the intelligent energy management of the complex ship energy system, achieve the carbon peaking and carbon neutrality goal and reduce the ship carbon emissions and ship operating costs, this paper proposes a distributed energy management method for ships entering and leaving ports based on polymorphic network considering computing power resources. Firstly, a polymorphic network-based energy management system for ships entering and leaving ports is proposed to enhance the information exchange between ship computing power, power and port power, simultaneously improve the communication quality and communication security among different modes. Secondly, in order to reduce the ship operating costs and port carbon emissions, the energy management model of ships entering and leaving ports is constructed considering computing power resources. Then, according to the ship’s berthing and departing operation modes, this paper uses the distributed algorithm to solve the energy management problem, and explores the impact on the ship microgrid when the ship’s data load changes. Finally, simulation results verify the effectiveness of the proposed algorithm.

Implementation of the ISM Code for MV Ships. Eastern Fair at PT Pelayaran Multi Jaya Samudera Belawan

This paper aims to investigate the Implementation of the ISM Code on the MV Ships. Eastern Fair at PT. pelayaran Multi Jaya Samudera Belawan. In writing this paper, the author utilizes methodology, during the Onshore Practice, the author collects materials from field observation methods and library methods. This is intended to gain a deeper understanding of the Implementation of the ISM Code on the MV Eastern Fair Ship at PT. Pelayaran Multi Jaya Samudera Belawan. The ISM Code is an International regulation developed by the International Maritime Oeganization (IMO) to ensure the safety of ship operation and the protection of the marine environment. By integrating the principles of the ISM Code into their operations, shipping companies can create a safer and more efficient environment for both ship crew and onshore personnel. Safety onboard is a shared responsibility. Safety plays a crucial role in the overall operation of ships. Facts shw that as much as 80% of all accidents can be attributed to human error. Furthermore, about 75-80% of these human errors can be linked to poor management systems. Safety encompasses the ship, crew, cargo, and the marine environment. The Implementation of the ISM Code plays a crucial role in ensuring that everything runs smoothly in terms of safety.

Online and Remote Training Experience in the Engine Room Simulator Training Courses using the IMO Model Course 2.07 Exercises

In the age of technology, changes are experienced in many areas such as globalization, information society, new basic technology, the spread of the Internet, and the realization of competition on a global scale. New methods used in education and training have been developed and kept up with technological developments. With the widespread use of the Internet, online and remote training has become more possible than ever. Technology was ready for online education and training for the past decades, yet the COVID-19 pandemic caused the traditional training to be disrupted and training institutions had to carry out studies for traditional training to be conducted online and remotely. This caused a paradigm change in the training methods. Online or remote education, even in training using the engine room simulator (ERS), has become more popular as an alternative to traditional classroom teachings, even though the pandemic is disappearing. Additionally, the progress in autonomous ships and recent regulatory studies indicates the importance of a better understanding of the engineering data in remote or autonomous ship operations. The authors have been offering Engine Room Simulator (ERS) training courses using online methods since the first semester of 2020, totaling six semesters of online training so far. This paper presents the online training process used in courses including the technological setup and method of teaching and assessment. Experiences and lessons learned by the lecturers in providing operational and management level ERS courses and feedback from students are examined and presented. The use of IMO Model Course 2017 Edition became helpful as it describes exercises for each training objective in accordance with IMO STCW 2010. An Engine Room Simulator (ERS) called Ship ERS or SERS™ was utilized as the remote training tool in the pilot implementation of these courses that were spread over two semesters. The advantages and disadvantages of online ERS training was presented and discussed. A particular and interesting observation has been that the students could have longer duration of exercise opportunities when using SERS™ in the remote training than in traditional ones.

Multiple Feature Extraction Long Short-Term Memory Using Skip Connections for Ship Electricity Forecasting

The power load data of electric-powered ships vary with the ships’ operational status and external environmental factors such as sea conditions. Therefore, a model is required to accurately predict a ship’s power load, which depends on changes in the marine environment, weather environment, and the ship’s situation. This study used the power data of an actual ship to predict the power load of the ship. The research on forecasting a ship’s power load fluctuations has been quite limited, and the existing models have inherent limitations in predicting these fluctuations accurately. In this paper, A multiple feature extraction (MFE)-long short-term memory (LSTM) model with skip connections is introduced to address the limitations of existing deep learning models. This novel approach enables the analysis and forecasting of the intricate load variations in ships, thereby facilitating the prediction of complex load fluctuations. The performance of the model was compared with that of a previous convolutional neural network-LSTM network with a squeeze and excitation (SE) model and deep feed-forward (DFF) model. The metrics used for comparison were the mean absolute error, root mean squared error, mean absolute percentage error, and R-squared, wherein the best, average, and worst performances were evaluated for both models. The proposed model exhibited a superior predictive performance for the ship’s power load compared to that of existing models, as evidenced by the performance metrics: mean absolute error (MAE) of 55.52, root mean squared error of (RMSE) 125.62, mean absolute percentage error (MAPE) of 3.56, and R-squared (R2) of 0.86. Therefore, the proposed model is expected to be used for power load prediction during electric-powered ship operations.

Techno-economic optimization of hybrid-electric power plants onboard cruise ships

This work aims at assessing energy, environmental and economic performances of hybrid-electric plants installed onboard large-size cruise vessels fuelled by Liquefied Natural Gas. Specifically, hybrid-electric power plants coupling combined gas electric and steam plants, reciprocating engines and lithium-ion battery pack are considered. By adopting cogeneration efficiency, carbon dioxide emissions, nitrogen oxide emissions and costs as objective functions, optimal performances were assessed by a Mixed Integer Linear Programming code, following a two-step procedure. First, the MILP code has been applied considering 8-days long, phase-mean operating profiles to identify an optimal design of the engine room. Second, power plant performances have been investigated by applying MILP code to hourly demand profiles, in order to provide more detailed insight on working schedule. Overall, as main outcome of the phase-mean analysis, the potential of combined gas electric and steam plants and lithium-ion batteries in promoting a more efficient energy utilization, reduced environmental impact and enhanced cost savings is proved. Specifically, combined gas electric and steam plant was shown to optimize objective functions during navigation and maneuvering, whereas batteries improved energy and environmental performances in the most demanding ship operating conditions. Furthermore, optimal sizing of lithium-ion battery pack was determined at 40 MWh, despite its economic competitiveness resulted to deeply rely on carbon tax. On the other hand, the hourly analysis showed that controlling the combined gas electric and steam plant and batteries according to a mixed spinning reserve-peak shaving strategy optimizes the performance of power plant even in port conditions, with almost no role played by reciprocating engines. In detail, in comparison to a reference non-hybrid electric power plant, batteries are shown to improve cogeneration efficiency and greenhouse gas emission savings by nearly 2%, whereas nitrogen oxide emissions and costs were reduced by 12% and 2%, respectively.

How do variations in ship operation impact the techno-economic feasibility and environmental performance of fossil-free fuels? A life cycle study

Identifying an obvious non-fossil fuel solution for all ship types for meeting the greenhouse gas reduction target in shipping is challenging. This paper evaluates the technical viability, environmental impacts, and economic feasibility of different energy carriers for three case vessels of different ship types: a RoPax ferry, a tanker, and a service vessel. The energy carriers examined include battery-electric and three electro-fuels (hydrogen, methanol, and ammonia) which are used in combination with engines and fuel cells. Three methods are used: preliminary ship design feasibility, life cycle assessment, and life cycle costing. The results showed that battery-electric and compressed hydrogen options are not viable for some ships due to insufficient available onboard space for energy storage needed for the vessel's operational range. The global warming reduction potential is shown to depend on the ship type. This reduction potential of assessed options changes also with changes in the carbon intensity of the electricity mix. Life cycle costing results shows that the use of ammonia and methanol in engines has the lowest life cycle cost for all studied case vessels. However, the higher energy conversion losses of these systems make them more vulnerable to fluctuations in the price of electricity. Also, these options have higher environmental impacts on categories like human toxicity, resource use (minerals and metals), and water use. Fuel cells and batteries are not as cost-competitive for the case vessels because of their higher upfront costs and shorter lifetimes. However, these alternatives are less expensive than alternatives with internal combustion engines in the case of higher utilization rates and fuel costs.

Advancing sustainability in the maritime sector: energy design and optimization of large ships through information modelling and dynamic simulation

This paper deals with a new energy design approach for ships to reduce the fuel consumption and the related environmental impact. The proposed method is based on the application of the Building Information Modeling (BIM) to Building Energy Modeling (BEM) technique. Specifically, by a BIM model of the ship a 3D physics-based model (BEM) can be suitably created. Then, by BEM the ship energy performance is simulated under real and dynamic operating conditions. By the presented method the whole design-to-delivery process of the ship can be simplified and speeded up with respect to traditional approaches, without losing reliability. As an example, HVAC systems design is easier through BIM since a high number of thermal zones can be effectively handled. Due to BEM, also the optimal design for exploiting waste heat recoveries of on-board combustion engines is easier and faster.To show the capability of the proposed approach a suitable case study was developed. Basically, it concerns the energy performance analysis of the Allure of the Seas, a 6000-passenger cruise ship operating in the Caribbean Sea. Two different scenarios for recovering the waste heat of the ship diesel generators are investigated. Simulation results highlight that significant primary energy saving can be obtained by optimizing the strategy to recover the available thermal energies (up to 600 MWh per trip), with a remarkable amount of avoided pollutant emissions (58, 0.06, 4.0, 0.2, 2.0 kg/km of CO2, PM2.5, NOx, HC, SOx, respectively).The presented new approach can be easily adopted to design and optimize the energy system of any new or existing ships, with the twofold aim to achieve economic savings and to fulfil environmental sustainability standards.

Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management

In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.

A solar energy-based shore side power system for a ferry service across the Suez Canal

ABSTRACT For more sustainable shipping operation in coastal areas and port cities, shore side power (SSP) systems are attracting widespread interest as a solution to reduce ship auxiliary engine emissions, noise and vibration. The potential of these systems can be further improved by integrating renewable energy into the electricity grid. However, the majority of prior research has focused on investigating SSP systems for large ports in large shipping hub countries. Therefore, in this study, SSP technology is investigated for an inland waterway in Egypt on the Suez Canal utilising real ferries operational data. Green electricity from solar sunshade structures is generated for the SSP system utilising the Egyptian excellent solar energy potential. For this study, the ferry diesel generator, battery and solar systems are modelled in MATLAB/Simulink environment to investigate the proposed SSP system. Results indicate that the proposed SSP system could eliminate annually 1420 tonnes of emissions as well reduce the grid CO 2 emissions by 1204 tonnes through the green electricity supplied to the grid. Moreover, the cash flow and net present value analyses have shown good profitability with a payback period between 7.4 and 12 years.

Analysis of ship accident resulting from bad weather conditions in the port of Khor Al-Zubair, Iraqi crane accident Aba Thar: a case study

The Arabian Gulf region is regarded as one of the world's most significant commercial areas, with large-scale and continuous marine shipments of commodities and goods to and from the Gulf. Iraqi ports are one of the most important ports in the Gulf, and they are a vital source of revenue for the Iraqi economy because of the money they bring in, as any stoppage of operations or damage to the ports may lead Iraq to lose a significant amount of money and halt shipping operations. Maritime accidents are one of the most significant concerns that have resulted in the suspension of maritime navigation and loading and unloading activities in Iraqi ports since there are various forms of marine accidents and many causes. With this study, we will look at maritime accidents caused by bad weather, as there have been many recent marine mishaps and ship collisions caused by bad weather, resulting in the loss of life and equipment and halting marine activities. We'll look at a few incidents, the meteorological conditions that contribute to them, and the link between them and the most likely cause of the accident. We will study an accident that occurred in the port of Khor Al-Zubair in 2018, which is considered one of the most important ports of Iraq, as it contains berths for importing and exporting goods, as well as on many of the Iraqi oil export berths, the accident of the Iraqi crane, Aba Thar. This report advises Iraqi port officials to limit activity in severe weather to reduce accidents that lead to loss of life and equipment and significant economic losses to the country. Keywords: Khor Al-Zubair, Maritime accidents, Aba Thar, Bad weather, The Arabian Gulf

Shining a light on AIS Blackouts with maritime OSINT

Maritime open source intelligence (maritime OSINT) can be used to capture information, critical to business operations, including when electronic systems like Automatic Identification Systems (AIS) fail. AIS is used to identify vessels in maritime navigation, to plan shipping operations, and ensure safety. This data is collected and sold by global aggregators to maritime operators and industry stakeholders. However, AIS communications are vulnerable to malicious cyber activity, changing legal landscapes, adverse physical conditions, or intentional intervention. It is the latter we will focus on in this report. Often, we won't understand the motivation behind a decision to either switch off or manipulate AIS data. However, this decision can have real world impacts, including on relationships between nation states, and the physical safety of maritime personnel. We believe maritime OSINT may represent an effective method to assist in the management of AIS failures. Maritime OSINT not only captures important maritime information when AIS fails, but it provides valuable context, giving us the who, what, and why for vessel operations. In our cast study, we will use maritime OSINT to track sanctioned Russian oil exports, demonstrating how OSINT can be used in this example to inform sanctions compliance programs.