Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems.
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This article describes a new product life cycle model that can be used by producers to improve safety and to prevent defective products from being placed on the market. The model has eight phases and the article describes and discusses the required safety-related issues in each phase. Analytical methods that should be used in the various phases are identified. Both consumer and industrial products are covered. The article outlines main product safety requirements with focus on European product safety legislation. The concept adequate safety is introduced as an acceptance criterion for the producer during the product development process, and factors that should be taken into account when deciding whether or not a product has adequate safety are discussed.
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Air emissions from fishing vessels must be reduced to comply with progressively stringent environmental regulations. Among the available solutions, liquefied natural gas (LNG) fuel may represent a promising solution, particularly from an environmental perspective. However, the use of LNG as a marine fuel creates different types of hazards than those that exist for traditional fuels. In addition, the increased complexity, safety requirements, and space required for LNG installation increase the capital cost. This article uses a systems engineering approach to clarify the technical aspects of LNG-fuelled systems, their potential implementation costs, and the expertise and training required to operate them safely. Ship owners can use such an approach to aid decision-making and trade-off analyses. Naval architects may also benefit from better information management. Finally, crews may better understand the logic behind the safety actions they are instructed to take.
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Autonomous systems, including airborne, land-based, marine, and underwater vehicles, are increasingly present in the world. One important aspect of autonomy is the capability to process information and to make independent decisions for achieving a mission goal. Information on the level of risk related to the operation may improve the decision-making process of autonomous systems. This article describes the integration of risk analysis methods with the control system of autonomous and highly automated systems that are evaluated during operation. Four main areas of implementation are identified; (i) risk models used to directly make decisions, (ii) use of the output of risk models as input to decision-making and optimization algorithms, (iii) the output of risk models may be used as a constraint in or modifying constraints of algorithms, and (iv) the output of risk models may be used to inform representations or maps of the environment to be used in path planning. A case study on a dynamic positioning controller of an offshore supply vessel exemplifies the concepts described in this article. In addition, it demonstrates how risk model output may be used within a hybrid controller.
Learning from accidents is decisive for establishing barriers to prevent future accidents and ensuring safety in socio-technical systems. Farmed fish escape is a major concern in the aquaculture industry and there is limited understanding of the accident scenarios involving fish escape. Hence, the systematic analysis of information and data about these types of near accidents and accidents may reveal contributing causes. The objective of this article is to present data and scenarios based on the investigation of 745 fish escape accidents and near accidents reported to the Norwegian Directorate of Fisheries from January 2006 to August 2019. The aim is to derive the direct and underlying causes and develop generic scenarios for fish escape, which can be used for improved reporting of near accidents and accidents. The paper discusses the limitations of current reporting systems, suggests a new categorization scheme for the Directorate of Fisheries to enhance the industry and authorities’ learning from fish escape events, and incorporates new types of fish farms emerging in the industry. Further, the study results can support barrier management and emergency planning, since they help understand the causes of fish escapes. This may be used for analyzing whether existing accident prevention strategies and barriers are sufficient or if additional barriers need to be implemented by fish farm operators.
<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>
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Technological innovations and new areas of application introduce new challenges related to safety and control of risk in the maritime industry. Dynamically positioned systems are increasingly used, contributing to a higher level of autonomy and complexity aboard maritime vessels. Currently, risk assessment and verification of dynamically positioned systems are focused on technical reliability, and the main effort is centered on design and demonstration of redundancy in order to protect against component failures. In this article, we argue that factors, such as software-requirement errors, human errors, including unsafe or too late decision-making, and inadequate coordination between decision makers, also should be considered in the risk assessments. Hence, we investigate the feasibility of using a systems approach to analyzing risk in dynamically positioned systems and present an adapted version of the system-theoretic process analysis. A case study where the system-theoretic process analysis is applied to a dynamically positioned system is conducted to assess whether this method significantly expands the current view on safety of dynamically positioned systems. The results indicate that the reliability-centered approaches, such as the failure mode and effect analysis, sea trials, and hardware-in-the-loop testing, are insufficient and that their view on safety is too narrow. This article shows that safety constraints can be violated in a number of manners other than component failures for dynamically positioned systems, and hence, system-theoretic process analysis complements the currently applied methods.
This paper presents an on-going research project focusing on the development of technology to enable autonomy in ROV operations. The project is a collaborative project between Norwegian offshore industry and academia. Currently, there is a large focus in research on the development of navigation, guidance and control for autonomous underwater vehicles (AUV). This is important as there will be a future demand for subsea inspection, maintenance and repair (IMR) operations with non-cabled systems. A future scenario is to have AUVs stationed on the seafloor in subsea garages. However, state of the art for IMR operations on the Norwegian Continental Shelf is to apply vessel supported ROVs in IMR operations. Efficiency in such operations will imply large cost and time savings. Increased autonomy enables the ROV operator to shift from manual to automatic control utilizing autonomous functions for a number of specific tasks. The research project presented in this paper is novel and the goal is to improve the capabilities of the ROV leaving the operator mainly to supervise operation. The paper discusses different aspects of the technology requirements. This may be useful for researchers working in the area of AUV research, relating this research to industrial needs. The presented project will develop novel integrated sensor platforms with robust perception methods and collision-free motion planning algorithms for subsea inspection and light intervention operations. Moreover, the project will also focus on subsea factory design enabling autonomous operations. The results will be tested, verified and demonstrated in full-scale test beds, as well as at an offshore location.
This article presents a virtual age model for decision support regarding life extension of ageing repairable systems. The aim of the model is to evaluate different life extension decision alternatives and their impact on the future performance of the system. The model can be applied to systems operated continuously (e.g., process systems) and systems operated on demand (e.g., safety systems). Deterioration and efficiency of imperfect maintenance is assessed when there is limited or no degradation data, and only failure and maintenance data is available. Systems that are in operation can be studied, meaning that the systems may be degraded. The current degradation is represented by a “current virtual age”, which is calculated from recorded maintenance data. The model parameters are estimated with the maximum likelihood method. A case study illustrates the application of the model for life extension of two fire water pumps in an oil and gas facility. The performance of the pump system is assessed with respect to number of failures, safety unavailability and costs during the life extension period.
