Applying reliability models to ship safety assessment (original) (raw)
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ENHANCING SAFETY IN SHIP'S CRITICAL SYSTEMS USING MARKOV MODELING
The current study uses reliability models for the improvement of the operation of a ship's "bilgewater separator" system. A "bilge-water separator" is a mechanism which cleans and inspects the ship's bilge water before it is discharged into the sea. Homogeneous continuous time Markov models have been used to record and estimate possible hazards and system failures in two different operational scenarios. If the photocell unit of the system fails, the ship may cause severe sea pollution. This study attempts to estimate the probability of sea pollution based on empirical data. In addition, the results of the model are compared with those of a system in which a second metering unit is added in an effort to to find out if this alteration improves the systems' efficiency.
Applying Performability Modelling in Maritime Risk Assessment
In the last decades Formal Safety Assessment is a methodology used for the estimation of maritime risk with the ambition to provide improved regulation for the improvement of safety at sea. Several studies have been shown that a great number of techniques can be efficiently applied at the field of maritime risk, such as Event Trees, Fault Trees or FMECA contributing mainly at the first two phases of FSA. This study analyzes the previous techniques and examines the case of applying homogeneous continuous time Markov chains at supporting the cost -benefit assessment phase, which is the fifth step of FSA. At the end a numerical example is given in the case of a containership company, to illustrate that useful information can be provided at the field of decision making in the maritime industry.
Journal of ship production and design, 2014
Historical marine accident/incident data remain severely underused in regulatory development as well as during design and operation. It is widely recognized that this is mainly the result of underreporting in commercially available databases and in databases maintained by national authorities. A factor further signifying this underuse is the evident improper reporting because most data are maintained as textual information requiring significant amounts of time and effort to distill and use the essential characteristics of accidents. Compounded with improved accessibility to an ever increasing amount of historical records, there is a need to develop the means that all the available information from marine accident/incidents is fully used in decision-making during development of new regulations, design, and operation. This article elaborates on the underlying causes for the current unsatisfactory state of affairs and details the description of the structures adopted for the development of appropriate marine accident databases using Bayesian Belief Networks as the platform for translating the information contained in the databases to probabilistic risk-based knowledge-intensive models. The article further explains the use of these models within a risk-based ship design framework, concluding with an example case study of application for fire safety onboard passenger ships.
A RISK FRAMEWORK FOR MARITIME TRANSPORTATION SYSTEMS
2013
Maritime accidents involving ships carrying passengers may pose a high risk with respect to human casualties. For effective risk mitigation, an insight into the process of risk escalation is needed. This requires a proactive approach when it comes to risk modelling for maritime transportation systems. Most of the existing models are based on historical data on maritime accidents, and thus they can be claimed of being reactive instead of proactive. This paper introduces a systematic, transferable and proactive framework estimating the risk for maritime transportation systems, meeting the requirements stemming from the formal definition of risk, which is adopted. The framework focuses on ship-ship collisions in the open sea, with a RoRo/Passenger ship (RoPax) being considered as the struck ship. It is developed with the use of Bayesian Belief Network, which effectively propagate the knowledge and understanding of the analysed system through the model. We expect this approach to assist the knowledge-based risk decision-making not only by informing the user about the risk but also about the effect of limited knowledge and understanding of the analysed system, on the risk.
A risk model for the operation of container vessels
2008
Commercial shipping of containerized goods involves certain risks for human safety and environment. In order to actively manage these risks, they must be identified, analyzed, modeled, and quantified. This requires a systematical analysis of design and operation of container vessels. Within the EU-funded research project SAFEDOR, a Formal Safety Assessment has been applied to establish the current safety level of generic container ships and to identify potential cost-effective risk control options. This paper describes a structured approach to develop the underlying high-level risk model. It is structured as risk contribution tree consisting of a series of fault trees and event trees for the major accident categories. Statistical analysis of casualty data is used to estimate the probability of occurrence. Finally, the summation overall individual risk contributions yields the current risk pro file for the operation of container vessels is presented as FN-curve.
Risk analysis of damaged ships – a data-driven Bayesian approach
Ships and Offshore Structures, 2012
An accident occurring at sea, though a rare event, has a huge impact both on the economy and the environment. A better and safer shipping practice always demands new ways to improve marine traffic and this essentially requires learning from past experience/faults. In this regard, probabilistic analysis of accidents and associated consequences can play a very important role in making a better and safer maritime transport system. Bayesian networks represent a class of probabilistic models based on statistics, decision theory and graph theory. This paper introduces the use of data-driven Bayesian modelling in risk analysis and makes a comparison with the different data-driven Bayesian methods available. The data for this study are based on the Lloyds database of accidents from 1997 to 2009. Important influential variables from this database are grouped and a Bayesian network that shows the relationship between the corresponding variables is constructed which in turn provides an insight into probabilistic dependencies existing among the variables in the database and the underlying reasons for these accidents.
Modeling risk in the dynamic environment of maritime transportation
Proceedings of the 33nd …, 2001
The Washington State Ferries are one of the largest ferry systems in the world. Accidents involving Washington State Ferries are rare events. However, low probability, high consequence events lead to difficulties in the risk assessment process. Due to the infrequent occurrence of such accidents, large accident databases are not available for a standard statistical analysis of the contribution of perceived risk factors to accident risk. In the WSF Risk Assessment, a modeling approach that combined system simulation, expert judgement and available data was used to estimate the contribution of risk factors to accident risk. Simulation is necessary to capture the dynamic environment of changing risk factors, such as traffic interactions, visibility or wind conditions, and to evaluate future scenario's that are designed to alter this dynamic behavior for the purposes of risk reduction or improved passenger service. This paper describes the simulation component of the model used in the Washington State Ferries Risk Assessment.
Global NEST International Conference on Environmental Science & Technology
Environmental disasters can maliciously affect property, human lives and even entire ecosystems. The magnitude and extent of such a disaster can lead to uncertainty about who is liable and how the restoration of the environmental damage will be achieved. The implementation of Risk Management processes enables us to combine our available information and resources and learn how to avoid such problems in the future. In shipping industry, oil spills from tanker vessels constitute the most severe threat to local and global ecosystem. When an oil spill happens, it usually spreads rapidly and is affected by weather and sea currents. Without prompt treatment it can cause huge disasters in the local aquatic ecosystem and human property altogether. This paper will assess the famous accident of Exxon Valdez based on Risk Assessment Methods and more specifically with the method of Root Cause Analysis to identify and measure the effect of each contributing factor to each accident and with Failur...
Journal of Marine Science and Engineering, 2021
No advance in navigation has yet to prevent the occurrence of accidents (incidents are always implied when we discuss accidents) at sea. At the same time, advances in accident models are possible, and may provide the basis for investigations and analyses to help prevent future adverse events and improve the safety of marine transport systems. In such complex socio-technical systems models that treat accidents as the result of a chain or sequence of events are used most commonly. Such models are well suited to damage caused by failure of physical components in relatively simple systems. Although these often include methods for modeling human error, they do not cover broader aspects related to the management of the organization using the means of transport itself (shipowners) nor errors that may occur in the design phase. In particular, they do not cover changes in the systems over time. The paper presents accident investigation approaches and uses a modified causal model to analyze a...