The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents

Technology and its innovative applications make life easier and reduce the workload on seafarers. Today's ship bridges have much more modern and integrated navigation systems than before, and the ship's handling and management have become much easier. However, nonconformities encountered in the use of technological devices may cause accidents. In this study, the effect of human factor related errors associated with the use of the bridge's electronic navigational devices on grounding and collision-contact accidents was investigated. Non-conformities obtained from 175 collision-contact and 115 grounding accident reports were qualitatively analysed by means of human factor analysis and a classification system. Afterwards, relationships between nonconformities and their probabilities were evaluated quantitatively via a Bayesian network method. As a result of the study, the accident network was revealed. This accident network summarizes how operating errors in the use of technological equipment cause accidents. Recommendations on the prevention of accidents caused by operating errors associated with the use of new technologies are finally given

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations. Document type: Articl

Risk assessment for transporting ammonium nitrate-based fertilizers with bulk carriers

Population growth has enhanced the need for agricultural products. Ammonium nitrate (AN)-based fertilizers are well known to increase product yield. Unfortunately, this type of fertilizer presents serious risks on account of its volatile nature. Accidents continue to occur despite international regulations and practices to eliminate or reduce risks arising from maritime transport to acceptable levels. In the present study, risks related to the transport of AN-based fertilizers were identified through root cause analysis of the M/V Cheshire accident. The relationships between the detected risks and probabilities were quantitatively evaluated using the Bayesian network method, and suggestions to prevent accidents caused by mistakes made during the transport of AN-based fertilizers were provided

Risk Assessment and Management for Maritime SAR and Oil Spill Response

This report summarizes selected publications that deal with maritime Search and Rescue (SAR) operations, winter navigation as well as oil spill response. In the first part of this report, the SAR capabilities, response times and effects of weather on Finnish Search and Rescue Units (SRUs) are evaluated. Besides this, the risk of oil spill and effects of winter conditions were evaluated. Two of the most relevant accident types – collisions consequences on oil tanker and RoPAx vessels – were evaluated. Both tankers as well as RoPax vessels are very common vessels in the Gulf of Finland, carrying thousands passengers or tonnes of oil. However, during the project it was found that there are currently no particularly reliable methods for assessing which sea areas are most prone to accidents. This highlights the need for future research in the methodology. Furthermore, a model is presented that describes the interaction between ships and the ice when navigating in an ice channel. This model helps to understand better the increased side forces and yaw that occurs in ice channel when compared to sailing in open waters. This can be used to train bridge personnel to better understand their ship's behavior under challenging ice channel conditions, thus decreasing risk.  A final model describes how fast an oil slick will spread in an ice channel as a function of factors such as the ice concentration and ice floe size, allowing for better estimation of how far oil will spread until effective clean-up measures can be taken

Analysis of factors affecting the effectiveness of oil spill clean-up: A bayesian entwork approach

Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including accident-related factors such as the type of accident, location, and environmental weather conditions, as well as emergency response-related factors such as available resources and response actions. To improve targeted and effective responses to oil spills resulting from ship accidents and enhance oil spill emergency response methods, it is essential to understand the factors that affect their effectiveness. In this study, a data-driven Bayesian network (TAN) analysis approach was used with data from the U.S. Coast Guard (USCG) to identify the key accident-related factors that impact oil spill clean-up performance. The analysis found that the amount of discharge, severity, and the location of the accident are the most critical factors affecting the clean-up ratio. These findings are significant for emergency management and planning oil spill clean-up efforts.Postprint (published version

Make Bow-tie Dynamic by Rethinking it as a Hierarchical Bayesian Network. Dynamic Risk Assessment of an LNG Bunkering Operation

In the present era, the spread of cyber-physical systems in the framework of the so-called Industry 4.0, is leading towards a complete automation of industrial processes, which are increasingly decentralized, smart, and require fewer and fewer frontline personnel. The risk assessment process is certainly not excluded from the revolution, and in perspective needs to be automatic, dynamic and linked with the conditions that emerge, moment by moment, in the life of a complex system. Analytical techniques can help in converting data in information and hence system knowledge to spot trends in operational performance, thus improving risk assessment quality. Even though the bow-tie approach is widely used within the context of complex systems, it still evidences several limitations, mainly connected to the actual assessment of likelihood and interdependencies in the fault and event trees. This paper shows how a bow tie analysis can be reframed as a Hierarchical Bayesian Network, where the probability distributions of the network nodes are updated with real time predictions during the operations. The proposed model was then applied to the risk assessment of a shore-to-ship LNG bunkering operation