Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate risks associated with complex systems. However, current approaches to construct and execute DPRA models are challenged by high execution time owing to numerous possible scenarios. This issue will affect the execution time of the model, which is in contrast with the aim of modeling. DPRA models must be sufficiently fast to assist decision-making processes in the required time. In this study, a new method is proposed to enhance the execution times of DPRA models. This method uses optimization algorithms to determine failure scenarios and sort scenarios based on their occurrence probabilities. The most efficient optimization algorithms, considering the nature of the DPRA models, are mixed-integer sequential quadratic programming, modified branch-and-bound algorithm, and modified particle swarm optimization, which are then compared and discussed. To validate the effectiveness of this method, a simple case study is presented. The results show the effectiveness of the method, which has high accuracy and reduces the execution time significantly (e.g. execution time of risk assessment of 16,464 possible behavior scenarios after an incident in a dynamic positioning system is one fifth of the conventional methods). A detailed supervised DPRA model of dynamic positioning systems and its application on three incidents that occurred in the Norwegian offshore sector in previous years is presented in a subsequent article (Part 2 of 1) (Parhizkar et. al.). Case study results confirm that the supervised DPRA method can be applied to other complex systems so that the dynamic probabilistic risk values can be evaluated quickly and accurately.
Working at sea presents a considerable fatal accident risk to the fishers of the fleet, far exceeding any average land-based job, as far as incident rate per man-labor year. This no-doubt is the result of placing an industrial workplace on a moving, oscillating and inherently unstable working platform subject to the vagaries of the sea. Here hazardous work is conducted in cramped workspaces, on uneven, slippery and cluttered decks, with operations involving heavy fishing gears and mobile rotational machinery. The purpose of this article is to analyze the circumstances of the 281 deaths reported to the Norwegian authorities from the Norwegian fishing fleet over the period 1990–2011. The aim is to determine important characteristics and traits in the statistics, and to facilitate and focus the devising of future preventative intervention strategies. The results indicate that there has been a considerable reduction in the number of fatalities in the fishing fleet of Norway over this time frame. The average incidence rate for fatalities during the 22year study period is 8.71 per 10,000 man years. Single, fatality occurrences are a more important cause of deaths in the fleet than multiple casualty events. The most significant modes of fatality are vessel disasters followed by man overboard events and drowning in port. The findings lead to a discussion on both the significance of the preventative measures put in place over the past two decades and the future directions and evolutions required for continued success in reducing fatalities in the Norwegian fleet.
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This paper proposes a novel method to transform the results of qualitative risk analysis into a numeric optimal control problem for autonomous ship navigation. Today, making autonomous high-level decisions replacing a crew onboard is considered difficult, in some part due to the complexity of managing the operational risks involved. Although human supervisors, e.g., located in remote operating control centers are still needed for safety and liability reasons, there is a growing demand for complex decisions to be made by the onboard control system itself, both during normal operations and in emergencies. This paper suggests general principles for how the results from systems-theoretic process analysis (STPA) can be transformed into a quantitative and computationally tractable optimization problem, solved by a MPC-based decision-making algorithm for autonomous navigation. The proposed method is demonstrated and evaluated by simulating an autonomous ship navigating in a coastal environment. It is concluded that the proposed method may serve as a reasonable and valuable bridge between the realms of qualitative risk analysis and numerical optimal control for risk-aware autonomous control and decision-making.
On March 23, 2019, the cruise ship Viking Sky with 1373 persons onboard almost grounded in severe weather conditions at Hustadvika, Norway. The near-miss incident had a major accident potential and clearly demonstrated the serious consequences that may result from loss of power and propulsion on a cruise ship close to the coastline in strong onshore winds and rough seas. This incident, combined with an increase in the cruise ship traffic in Norwegian waters, calls for risk reduction measures to be identified, evaluated, and implemented for the industry and society. It is not feasible to have sufficient search and rescue resources to handle worst-case scenarios with large cruise ships, and therefore proactive mitigation measures are necessary. This paper presents the challenges with cruise ship safety and emergency preparedness and gives recommendations for risk reduction measures. The paper is based on a White paper made by a Committee appointed by the Norwegian Government, with members (including the authors) and contributions from the industry, authorities, and academia.
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The marine environment is vast, harsh, and challenging. Unanticipated faults and events might lead to loss of vessels, transported goods, collected scientific data, and business reputation. Hence, systems have to be in place that monitor the safety performance of operation and indicate if it drifts into an intolerable safety level. This article proposes a process for developing safety indicators for the operation of autonomous marine systems (AMS). The condition of safety barriers and resilience engineering form the basis for the development of safety indicators, synthesizing and further adjusting the dual assurance and the resilience based early warning indicator (REWI) approaches. The article locates the process for developing safety indicators in the system life cycle emphasizing a timely implementation of the safety indicators. The resulting safety indicators reflect safety in AMS operation and can assist in planning of operations, in daily operational decision-making, and identification of improvements. Operation of an autonomous underwater vehicle (AUV) exemplifies the process for developing safety indicators and their implementation. The case study shows that the proposed process leads to a comprehensive set of safety indicators. It is expected that application of the resulting safety indicators consequently will contribute to safer operation of current and future AMS.
Human fatigue is influencing the risk of maritime accidents. A ship`s bridge sailing in trafficked waters is a multi-causal and complex system. Decisions made on the bridge may have invisible and delayed consequences. This article explains how and why accident investigation reports can be used to construct easily updateable quantitative risk assessment models which take human factors into account. The article then makes use of the Barrier and Operational Risk Analysis (BORA) method to illustrate an example by conducting a case study of the effect of human fatigue on maritime groundings. The case study indicates that lowering the probability of human fatigue and alcohol misuse on board does not decrease the probability of grounding as much as lowering the probability of barrier failures (lookouts, watch alarms and VTS radio calls). Less than adequate scores on human fatigue, safety climate and manning levels have a stronger effect on the probability of grounding then adequate scores.
<p>Enabling higher levels of autonomy requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This article presents an approach to improve this ability for a robotic system executing a series of independent tasks by using a dynamic decision network (DDN). A case study of an industrial inspection drone performing contact-based inspection is used to demonstrate the capabilities of the resulting system. The case study demonstrates that the system is able to infer the presence of internal faults and the state of the environment by fusing information over time. This information is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Lastly, the case study demonstrates that evaluating past states with new information enables the system to identify and counteract previous sub-optimal actions. </p>
This paper focuses on path planning problems integrating the risk of collision. This challenge is crucial regarding the use of autonomous underwater vehicles (AUVs) for inspection, maintenance and repair operations. The solving of this problem in a reasonable amount of time enables integration of path planning in the AUV control architecture and, by this way, enhances its autonomy capability. Classical approaches rely on the A-Star algorithm to solve this problem, but the heuristic associated to the collision risk appears to be inefficient in some cases. Based on the hierarchical technique, i.e., HPA-Star, another approach is proposed. It leads to paths close to the optimal ones calculated in a faster way. The performances are illustrated in the context of a multi criteria optimization: minimal length and minimal risk path planning.
With higher autonomy in maritime systems, tasks and responsibilities are moved from the human operator to software, increasing the complexity and the importance of safe and reliable functionality. Software failures, however, may be introduced from the early life cycle phases intentionally or unintentionally, and these must therefore be mitigated by safe and secure design approaches. A challenge is that existing methods are not particularly well-suited for analyzing software risks. Thus, the objective of this paper is to propose a systematic and efficient software failure identification approach by extending the Systems-Theoretic Process Analysis (STPA) with a software failure taxonomy and the System Modeling Language (SysML). This enables the control structure in STPA to cover both the dynamic and static aspects of the software functions. Combined with an implementation platform independent questionnaire, this gives a more systematic and guided search for potential software failures than existing approaches. To demonstrate the proposed approach, a case study on a ferry's navigation system that operates in manual control or semi-autonomous mode is performed. In the case study, the focus is on creating an avoidance map data structure, including both moving and static obstacles to be avoided by the ferry, and the subsequent process of collision risk warning calculation. Software failures are identified and evaluated in collision scenarios where the ferry operates under foggy conditions. The paper shows that the proposed systematic approach provides an improved process for identifying and analyzing critical software failures. This facilitates enhanced risk mitigation in the design and testing phases contributing to autonomous systems' safety and security.
