Determination of the Ship Engine Room Pipeline Failure Risk Rating with Fuzzy-Bayesian Network

Abstract e systems on the ship have various uids in terms of theirphysical and chemical properties. e transfer and storageof these uids are carried out with the help of pipelinesystems. Although maintenance is carried out periodicallya er the rst installation, it is impossible to see what ishappening inside the pipe systems. For this reason, therisk levels of these systems should be analyzed so as not tobe faced with hidden hazardous situations that may arisesuddenly. Although the design of the ship system is withinthe framework of the rules and regulations, practicalknowledge is required in the operational process. eseexperienced data can be easily obtained from the personnelwho worked on the ships, which means that they can directlyreach the source.In this study, fuzzy logic is used in the data collectionprocess, and the Bayesian network structure is organizedto analyze the hazard probability of pipeline failures.Vibration, maintenance breakdowns, and improperhandling of pipelines are the leading causes of pipelinefailure. e importance of these is obtained from theresult values given by the modeled Bayesian Network. is research includes the root causes that trigger pipelinefailures, and these reasons have been assessed in terms oftheir level of impact.</p

Risk assessment of the Ship steering gear failures using fuzzy-Bayesian networks

Accidents caused by steering gear malfunctions, especially during port berthing maneuvers, the strait, and canal crossings, can lead to hazardous consequences on the environment and human life. This study aims to provide the risk evaluation and investigation of the root causes of the steering gear failures on board using Fuzzy Bayesian Networks. To determine the effects of root causes on steering gear failures, the Bayesian Network was built in the NETICA software. Running several scenarios on the network are ensured the investigation of how the root reasons affect the problem. Prior and conditional probabilities obtained from meetings and interviews with six different experts were used to apply Bayesian inference. Sensitivity analysis, forward propagation analysis by applying the best- and worst-case scenarios, and validation of the network were conducted. Results depicted that the functionality of the electrical and mechanical line components is essential to preventing the breakdown of the steering gear system. The most significant contributor to component-related electrical failures is loose or corroded wiring and connections, whereas hydraulic oil-sourced errors have a significant impact on equipment wear and malfunction. The probability of a steering gear accident is assessed to be 13.7% under the best-case scenario and 79.1% in the worst-case scenario

Marine Enviroment Protection New technologies on oil spill response industry

The number of oil spills from tankers has decreased dramatically over the last 25 years although seaborne oil trade increased significantly. However recovery and cleaning rates of oil pollution did not show a marked improvement through this period. The effectiveness of response operations which has a close relationship with technology, infrastructure, and personnel are only measured with quantity of oil recovered and the area cleaned. There are so many response techniques have proven their success in laboratory and controlled field experiments in marine conditions. There is wide range of challenges encountered when recovering of oil such as old technology, distance, lack of infrastructure, and moderate environmental conditions in marine environment. Equipment, vessels and personnel would need to be mobilized over potentially vast distances to overcome these challenges. Time is one of the most important variables which have direct effect on the response operation. Reducing risk and time losses can be provided with new technologic infrastructure. In this study the limitations of the various response techniques are summarized. Then the factors that determine the seriousness of marine oil spills and the fundamental technical difficulties of combating them at-sea are discussed. Within this scope the relationship between technological developments of infrastructure and effectiveness of oil spill response operations are mentioned.</p

INVESTIGATION OF GROUNDING ACCIDENTS IN THE BAY OF IZMIR WITH THE APPLICATION OF ROOT-CAUSE ANALYSIS

City of Izmir as the third largest city of Turkey, is the opening gate of Anatolia to the world through Aegean Sea. The city with its great volume of cargo capacity plays crucial role both in international and domestic sea trade. The Port of Alsancak located at the inner part of the bay is the largest seaport of Aegean Region in terms of annual loading capacity. The main objective of this research is to find the root-causes of accidents that have resulted in the groundings in Bay of Izmir. To do this, the Root Cause Analysis methodology was carried out on the accidental data provided by Turkish Main Search and Rescue Coordination Center (TMSRCC). Between 2001 and 2016, a total of 24 ships grounded at the entrance of Yenikale due to shallow water conditions, which is regarded as the riskiest point in terms of groundings. In this study, the Fault Tree Analysis (FTA) method which is the one of the most preferred root cause analysis methods was used. As a result, it was found that equipment failures and geographical factors are the main reasons of grounding accidents in Bay of Izmir. In order to eliminate these causes, necessary precautions have been offered and suggestions for further studies have been made

Flag Choice Behavior in the Turkish Merchant Fleet: A Model Proposal with Artificial Neural Network Approach

Shipping companies have to take several strategic decisions about the vessels that perform transportation activities. The most important of these strategic decisions is “Flag Choice”. This decision given by the company is shaped under the light of external and internal factors. In this paper, initially, the factors which affect flag choice decision of shipping companies and ship owners who play an important role to handle Turkish merchant fleet are determined. Then, the relation and association status of the factors which have significant impacts on this decision are displayed with data mining application. Artificial Neural Networks (ANN) application is realized with the obtained outputs and a model is proposed for flag selection decision. It is expected that the results of the study provides certain outcomes and guidelines for related organizations dealing with shipping operations as well as suggestions for effective and efficient coordination among the relevant institutions