1,721,077 research outputs found
Ship machinery condition monitoring using vibration data through supervised learning
No full textThis paper aims to present an integrated methodology for the monitoring of marine machinery using vibration data. Monitoring of machinery is a crucial aspect of maintenance optimisation that is required for the vessel operation to remain sustainable and profitable. The proposed methodology will train models using pre-classified (healthy/faulty) data and then classify new data points using the models developed. For this, vibration points are first acquired, appropriately processed and stored in a database. Specific features are then extracted from the data and stored. These data are then used to train supervised models pertinent to specific machinery components. Finally, new data are compared against the models developed in order to evaluate their condition. The above will provide a flexible but robust framework for the early detection of emerging machinery faults. This will lead to minimisation of ship downtime and increase of the ship’s operability and income through operational enhancement
A Data-Driven Model for Rapid CII Prediction
Full text linkThe shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation
Improving ship maintenance : examining the case of pipe laying vessels
No full textMaintenance management in shipping industry is continuously being improved in order to ensure that structures and machinery are operated and maintained in accordance with the established rules and regulations. That is enforced by the current condition in maritime industry to apply similar methods of maintenance that have effectively been applied in other industrial fields. More specifically, new maintenance strategies that combine new tools, such as reliability analysis and condition monitoring, with the traditional well-established maintenance methods, have been introduced. The study herein presents a review of the modern maintenance methods in shipping, as well as a review of the reliability assessment tools, such as Fault Tree Analysis (FTA), Failure Modes, Effects and Criticality Analysis (FMECA) and Markov Analysis. Further on, Fault Trees with time-dependant dynamic gates are used to model the pipelay system of two vessels in order to examine their reliability performance through FTA. Main outcomes are the identification of the reliability behaviour of the systems and their subsystems, as well as of their most critical failures. Based on those results, substantial conclusions about the improvement of the maintenance of subject systems are derived
Establishing an innovative and integrated reliability and criticality based maintenance strategy for the maritime industry
Full text linkDeveloping a methodology for interoperable simulation tools for optimising sustainable ship operations
No full textAmidst regulatory updates from the International Maritime Organisation (IMO) and statutory requirements aiming for environmental sustainability, the shipping industry is rapidly shifting to-wards greener operational profiles. Key strategies towards net-zero include the adoption of energy-saving technologies, green fuels and onboard carbon capture systems, which are all significantly complex ventures, especially in the case of retrofits. The complexity is managed by utilising advanced process modelling and simulation software. However, as the userbase of such software grows, so does the need of interoperability. In the case of process simulation environments, reusable components can be developed by following the CAPE-OPEN interoperability standard, which is utilised by established commercial solutions. In this paper, a methodology for creating compliant models of marine energy systems is presented and an example of its utility is provided, in the form of a thermodynamic property and equilibrium calculator which can accurately simulate exhaust gases. This software will be used in future research to model cryogenic carbon capture systems
Enhancing bridge simulation training programmes with the application of maritime aids for emergency responses
No full textApplication of artificial neural network and dynamic fault tree analysis to enhance reliability in predictive ship machinery health condition monitoring
Full text linkThe electric power generation system of most ships is powered by a group of diesel generators generally with redundancy to accommodate peak load periods or critical situations. Blackouts onboard ships portents a potential danger to navigation as well as the security and safety of the ship. Thus, understanding the factors affecting the reliability of individual diesel generators and the most critical component to failure is key to ensuring reliable performance of the generators. Therefore, this study was conducted on diesel power generation plant consisting of four Marine Diesel Generators onboard an Offshore Patrol Vessel (OPV). Findings indicates relatively low reliability, of less than 60 per cent within the first 24 months of the 78 operational months data analysed. Similarly, reliability importance measures were adopted to identify Critical components which contribute at least 40 per cent of failures on the sub systems of the diesel generators. The use of dynamic spare gates in the dynamic fault tree analysis has highlighted possible improvements through maintenance action or use of sensors to improve sub-system as well as individual diesel generator’s reliability. Additionally, Artificial Neural Networks classification using unsupervised learning was conducted to identify patterns in the data that signifies the onset of performance degradation in the diesel generators
A novel, data-driven heuristic framework for vessel weather routing
Full text linkFuel Oil Consumption (FOC) constitutes approximately two-thirds of a vessel’s voyage costs and profoundly correlates with the adversity of the weather conditions along its route. Furthermore, increased FOC also leads to increased emissions. As shipping is turning page towards a greener, more sustainable future, it is crucial to leverage key insights from past routes in order to identify approaches that minimise both the financial cost of operations and their Green House Gas (GHG) footprint. This study presents a novel framework for vessel weather routing based on historical ship performance and current weather conditions at a discretised grid of points in conjunction with a data-driven model that can predict main engine FOC. Subsequently, a modified version of Dijkstra’s algorithm that has been fitted with heuristics is applied recursively until an optimal route is obtained. The efficacy of the proposed framework is demonstrated through a case study concerning the optimal route selection for a 160,000 tonne DWT crude oil tanker sailing between the Gulf of Guinea and the Marseille anchorage. In this case study, an푅2of 89.4% was obtained while predicting the vessel’s FOC and five optimal routes were identified and ranked for two sailing speeds corresponding to different operating profiles, i.e. ballast and fully loaded
Implementing unsupervised learning algorithm for marine engine data clustering applications
No full textData preparation and processing is of great importance in a ship condition monitoring tool, as inaccurate and misinterpretation of data can significantly affect the condition monitoring accuracy and performance. Data for performance parameters related to the case study of a Panamax container ship main engine are clustered using an artificial neural network, the Self-Organizing Map (SOM). Neighbouring clusters are compared through a distance metric to examine the existence of data similarities. Additionally, the SOM has a supplementary functionality of identifying data clusters exceeding thresholds, consequently providing diagnostics connected to a Failure Mode and Effects Analysis (FMEA) for the main engine, providing valuable insight and information regarding potential faults. The SOM model is validated through actual data extracted from the case study. Moreover, simulated data representing data exceeding alarm levels for the engine fuel oil system demonstrate the capabilities of the SOM clustering process in combination with the associated FMEA results
Analysis of time series imaging approaches for the application of fault classification of marine systems
Full text linkArtificial Intelligence (AI) can enable better coordination between ships by enhancing decision-making processes through the optimisation of marine vessels' communication technologies and the gathering of information via Internet of Ships (IoS). Although some efforts have been made to detect faults and malfunctions that can occur in marine systems, there is a lack of analysis and formalisation of fault identification (a.k.a. fault classification) approaches; the aim of which is to provide a comprehensive description of any considered fault type and its respective nature. To contribute to this unexplored field within the shipping sector, an analysis of a total of seven time series imaging approaches (Gramian Summation Angular Field (GASF), Gramian Difference Angular Field (GADF), Markov Transition Field (MTF), Markov Transition Matrix (MTM), Recurrence Plot (RP), compound of GASF-GADF-MTF, and compound of GASF-GADF-MTF-MTM-RP), is performed, as these approaches have demonstrated their ability to identify fault patterns that can not be perceived when considering the original time series data. The resulting images are presented as input in a Convolutional Neural Network (CNN) for the performance of the classification task. As part of this analysis, a case study on the turbocharger exhaust gas outlet temperature parameter of a bulk carrier's main engine is also introduced. Promising results are obtained when the distinct time series imaging approaches are combined, as the compound GASF-GADF-MTF-MTM-RP achieved the maximum accuracy for the analysed case study. Such results evidence the need of exploiting the field of time series imaging for the identification of faults
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