LEE- Deniz Ulaştırma Mühendisliği Lisansüstü Programı
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Yazar "Çelik, Metin" ile LEE- Deniz Ulaştırma Mühendisliği Lisansüstü Programı'a göz atma
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ÖgeA conceptual approach for design and development of serious games in maritime domain(Graduate School, 2024-01-19) Gürbüz, Süleyman Cihan ; Çelik, Metin ; 512182005 ; Maritime Transportation EngineeringAround 80,000 merchant vessels which are manned by about 1.6 million seafarers transport around 90 percent of world trading products. Transportation of important products such as food and medical supply during the Covid-19 pandemic has even spotlighted the vital role of seafarers. International Convention on Standards of Training, Certification and Watchkeeping (STCW) (referred as STCW in this study), which has been issued by International Maritime Organization (IMO), defines the training and competency standards of the seafarers. It has been argued that there is an important gap between required on-board competency levels of seafarers and their actual levels of competency. Despite the regulatory and technological advancements in shipping, human error still plays a major role in more than 80% of shipping accidents. For enhanced competency development of seafarers, maritime industry needs to find cheaper, accessible and more flexible methods of practical education and training. Serious gaming, as a technology-enabled instructional method, offers an important potential for maritime domain as it provides interactive and authentic learning environments. In this regard, main objective of this study is proposing a holistic conceptual approach for effective design, development and utilization of serious games in maritime domain. More specifically, this study intends to provide the academicians and practitioners with a foundational basis for creating and using maritime serious games. For this purpose, a systematic literature review on serious game design approaches with a special focus on future skill development is firstly conducted. In this review, 32 serious game design models which provide practical steps for serious game design are selected. It is found that 8 (25%) of these design approaches support at least one future skills, among which problem‐ solving as well as collaboration and teamwork are the most commonly supported ones. It is also discovered that clear goals and interactivity, used in 6 (75%) and 5 (63%) of the 8 design approaches respectively, are the most commonly implemented game design elements. Considering the significant literature gap on the implementation of serious games for future skills development, this literature review consequently provides valuable insights for the game designers, software developers, educational technology researchers, and engineering educators in various domains. After that, Serious Game Design for Maritime (SGDM), a holistic model to support the design of maritime serious games is proposed. Using the SGDM model, MARITIME LEADERS at SEA (ML@S), a 3D serious game to enhance the leadership and teamwork skills of young seafarers and maritime students, has been prototyped. ML@S game is conceptualized as a module of the "Maritime Gamentor" platform. Using the SGDM model, TASK-BASED RISK ASSESSMENT AT SEA (MRA@S) game is also designed and prototyped for task-based risk assessment training in preparation for the Ship Inspection Report (SIRE 2.0) inspections of Oil Companies International Marine Forum (OCIMF). Proposed model (SGDM) as well as the explained methodology can be followed by technology initiatives, game designers, and researchers for development of similar maritime serious game modules on soft skills and technical skills. Besides, functions of the Maritime Gamentor platform can be further extended in the maritime domain by adding similar serious game based training modules. After prototyping the games, an experimental study was conducted for analyzing the efficiency of the ML@S game and proposed SGDM model. It can be concluded from the experimental study that the game was tested successful by the students in all 4 categories (motivation and engagement, game effectiveness, game clearness, future use). This being the case, it can be put forward that the developed ML@S game can be used as a means of leadership education and training. Looking at the broader picture in the study, it was proved that the SGDM model can be practically applied to design successful maritime serious games. In sum, serious games can provide maritime students and young seafarers with the practical education and training they need in a cost effective way. For this reason, it is believed that the proposed approach can be followed by technology initiatives, game designers, and researchers for development of similar maritime serious game modules on soft skills and technical skills. Consequently, this research intents to contribute to safety, security and environmental protection in maritime domain by providing an insight into enhanced competency development.
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ÖgeA holistic data analytics approach to ship inspection reporting(Graduate School, 2023-08-08) Biçen, Samet ; Çelik, Metin ; 512192016 ; Maritime Transportation EngineeringMaritime inspection analysis has become an emerging topic in recent years, as practical solutions are sought to improve the pre- and post-inspection analysis in shipping operations. With a focus on finding practical solutions to enhance the pre- and post-inspection process in shipping operations, such as The Oil Companies International Marine Forum (OCIMF) Ship Inspection Report Programme (SIRE), RightShip, The Tanker Management Self-Assessment (TMSA), Chemical Distribution Institute (CDI), there is a growing demand for effective methodologies. The objective of this research is to enhance this field by examining documented observations through the utilization of both natural language processing (NLP) and machine learning (ML) methods. The main goal of this study is to make a valuable contribution to this field by analyzing reported observations. This will be accomplished by employing a combination of natural language processing (NLP) and machine learning (ML) techniques. Additionally, a statistical algorithm model will be utilized to conduct analysis using demographic data. To achieve the objectives of the study, a robust methodology was developed, which leverages the benefits of the American Bureau of Shipping Maritime Root Cause Analysis Tool (ABS-MARCAT). This tool enables the systematic initiation of a potential causes database, incorporating a substantial number of 2383 observations. By employing ABS-MARCAT, the study aims to provide a comprehensive foundation for analyzing and understanding the causes behind reported observations and determining corrective and preventive action tips for elimination of this causes. One of the key contributions of this research is the development of an NLP-based ML algorithm. This algorithm plays an important role in predicting the causes of new entries and determining corrective and preventive action tips in the inspection report's observations. The algorithm's performance demonstrates high accuracy, with results varying between 0.90 and 0.98 across different causation categories. Such accuracy is promising, as it allows for effective identification and classification of causes, providing valuable insights for decision-making in the maritime industry. Another important contribution of this research is the statistical algorithm model that can produce frequencies of causes based on independent variables such as ship name, inspector name, oil major company name and port name. The statistical algorithm model provides predictions about the areas to be considered according to the information required before the inspection. By presenting the frequencies of the cause categories according to the independent variables, it provides a decision support system in the process of predicting the inspection parts to be considered before the inspection. Another important contribution of this research is to suggest corrective and preventive action tips to eliminate the causes of the observations after the causes are identified. The corrective and preventive action tips determined by maritime experts will add a different dimension to the decision-making processes by providing solution suggestions after the analysis of the inspection reports. The pre- and post-inspection analysis model developed in this study holds great potential for enhancing fleet safety and efficiency. By providing maritime executives with an accurate tool to analyze inspection data, it enables them to make informed decisions and take proactive measures to address potential issues. The model serves as a third-party solution for the shipping industry, offering an independent and reliable means of analyzing and assessing inspection data. Looking ahead, future studies are planned to further refine and expand this model. The aim is to conceptualize it as a platform as a service (PaaS) offering, which would enable wider access and utilization by stakeholders in the maritime industry. By transforming the model into a PaaS, it has the potential to become a valuable resource for multiple organizations, facilitating improved fleet safety, operational efficiency, and informed decision-making. In conclusion, this study addresses the emerging field of maritime inspection analysis by developing a robust pre- and post-inspection analysis model. Through the integration of statistical algorithm model, NLP, ML, and the MARCAT tool, the study offers a holistic approach to analyzing reported observations and statistical data. With its high accuracy, the model has the potential to make a significant contribution to the improvement of fleet safety and efficiency. Furthermore, by conceptualizing it as a platform as a service, the study paves the way for wider adoption and application of the model within the shipping industry.
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ÖgeA prescriptive analytics approach towards critical ship machinery operations(Graduate School, 2024-07-09) Yiğin, Barış ; Çelik, Metin ; 512182001 ; Maritime Transportation EngineeringShipping handles more than 70% of global trade, is a pillar of the supply chain. To ensure safe, reliable, and environmentally responsible operatins, shipowners and operators must maintain their vessels' operational status at all times. Maintenance standards are essential for keeping both main and auxiliary machinery in optimal condition, thereby ensuring reliable and safe operations. These programs aim to maintain high performance with minimal impact on service, recognizing that the cost savings from effective maintenance program can prevent drawbacks due to machinery faults. The main objective of machine maintenance is to maximize availability by extending the service life of ship machineries and eliminating potential failures by early detection. This involves finding the finding the optimum maintenance strategy, as even minor failure can cause irreversible damage to the entire system if not promptly addressed. Given the complexity and interdependencies within marine systems, a proactive maintenance approach is crucial. Due to scarcity of labeled data and anomalous data, the research question of anomaly detection always attracted interest from academia and industry. Implementing anomaly detection technologies is a challenging task in marine systems due to their complexities and external factors. To address these challenges, this study proposes a prescriptive analytics framework that combines predictive analytics and decision support systems. This framework leverages data collected from various sensors installed on ship machinery to monitor performance and detect anomalies. One of the key innovations of this research it employes data augmentation techniques to generate realistic synthetic failure data, further enhancing the robustness of predictive models. The implementation of this prescriptive maintenance framework involves several steps. First, a comprehensive Failure Mode and Effect Analysis (FMEA) is conducted to identify potential failure modes, cause of failures and effects of failures. This analysis helps prioritization of the maintenance activities based on the criticality of different failure modes. Next, with the use of data augmentation technique called Generative Adversarial Network, synthetic data generation carried out to create faulty data information. This faulty data generation step enhance the training pool before the next step of anomaly detection process. In order to perform anomaly detection, six different classifiers namely, logistic regression, decision trees, random forest, K nearest neighbor, AdaBoost and XGBoost algorithms trained and validated using historical data and the generated synthetic data. Data set used in this study includes real time data collected from field on a diesel generator installed on a 310,000 DWT oil tanker. The field data collection took place over a six month period and it includes 33 features and 259,200 row data. Findings from the study yield promising results achieving 83.13% accuracy with use XGBoost algorithm and other ranging between 67% to 81%. Finally, a decision support system is integrated to provide actionable recommendations to ship operators, optimizing maintenance schedules and resource allocation. The results of the field study conducted as part of this research demonstrate the effectiveness of the proposed framework. Ships equipped with the prescriptive maintenance system has a significant potential for reduction in unexpected machinery failures, maintenance cost and autonomy of decision in case of anomalies. The system also offers improvement of overall operational efficiency and reliability of the ships. In conclusion, prescriptive maintenance the pinnacle of modern maintenance strategies, offering returns in terms of equipment reliability, safety and operational efficiency. Although installation of data acquisition systems may require initial investment, the benefits include reduced operational distruption and optimized maintenance budgets, making it a valuable approach for the maritime industry.
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ÖgeA statistical analysis to control pH level of main engine cooling system onboard unmanned surface vessel(Graduate School, 2023) Sel, Hakan ; Çelik, Metin ; 783862 ; Maritime Transportation Engineering ProgrammeThe interest in unmanned ships has been increasing rapidly all over the world recently. This new field, which will enter our lives in the near future, brings with it many question marks. The most important issue in mind is how to reduce the risks of unmanned ships and how safe they will be. As it is known, the concept of safety has a very important place in the maritime sector. In order to ensure the concept of safety with all the systems/devices on the ship, the issue of maintenance has become one of the items at the center of the maritime industry. In order to carry out the maintenance operations of unmanned ships, it is necessary to establish and safely use autonomous maintenance systems. However, due to the fact that unmanned ships have not been actively used yet, studies in the field of autonomous maintenance needs are limited. In the light of this information; in this thesis, it is aimed to develop a diagnostic model for autonomous ships that allows controlling the pH (potential of hydrogen) value in the cooling water system, which is one of the maintenance steps of the diesel engine in an unmanned surface vehicle. As the first step of a statistical analysis to control the pH level of the diesel engine cooling system in unmanned surface ships, variables will be determined to carry out data collection. Managing an advanced statistical solution for this operational situation is crucial. Association Rule Mining (ARM) approach will be used in order to reveal the interconnectedness of the determined variables. The number of diesel engine variable will be defined to create an operational data frame. Then, together with the ARM approach, the factors affecting the pH value will be determined. Thus, development of a diagnostic model for autonomous ships that can control the pH value of the diesel engine cooling water of unmanned surface ships will be developed. With this thesis, it is aimed to achieve the following objectives; i.) to determine the operating values of the diesel engine of unmanned ships, ii.) to reveal the connections between the operating values by integrating the ARM approach, iii.) to determine the effect of these values on the pH value of the cooling water, iv.) to determine the methods to control the pH value autonomously. As a result, it is thought that this thesis will contribute to engineers, marine researchers and professional sailors about autonomous maintenance systems to be installed on unmanned surface ships.